R programming code, we use
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Meta-Analysis
install.packages("tidyverse")
install.packages("meta")
install.packages("metafor")
library(tidyverse)
library(meta)
library(metafor)
hidemeta_FB_Farm_S <- metaprop(Obesity_Prevalence, Total_Sample,
studlab=AuthorsName_Year, sm="PFT",
data= Obesity_associated, method="Inverse", method.tau="DL")
summary(hidemeta_FB_Farm_S)
forest.meta(hidemeta_FB_Farm_S, layout="RevMan5", xlab="Co-morbidity associated with obesity prevalences",
comb.r=T, comb.f=F, xlim = c(0,1), fontsize=10, digits=3)
hidemeta_FB_Farm_S <- metaprop(Total_Sample,Obesity_Prevalence,
studlab=AuthorsName_Year, sm="PFT",
data= prepandemic, method="Inverse", method.tau="DL")
summary(hidemeta_FB_Farm_S)
forest.meta(hidemeta_FB_Farm_S, layout="RevMan5", xlab="Pre-pandemic obesity prevalences",
comb.r=T, comb.f=F, xlim = c(0,1), fontsize=10, digits=3)
hidemeta_FB_Farm_S <- metaprop(Total_Sample,Obesity_Prevalence,
studlab=AuthorsName_Year, sm="PFT",
data= after_pandemic, method="Inverse", method.tau="DL")
summary(hidemeta_FB_Farm_S)
forest.meta(hidemeta_FB_Farm_S, layout="RevMan5", xlab="After/During pandemic obesity prevalences",
comb.r=T, comb.f=F, xlim = c(0,1), fontsize=10, digits=3)
Hysterectomy NHANES Survey Data
install.packages(c('tidyverse', "survey"))
library(tidyverse)
library(survey)
library(gtsummary)
#MUTATE
nhanes.mod5<- X2013_14 %>% mutate(
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " , DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree", DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married/Living with Partner" , DMDMARTL=="2"~"Widowed/Divorced/Separated" , DMDMARTL=="3"~ "Never married"),
HSD010_char=case_when(HSD010=="1"~"A",HSD010=="2"~"B",HSD010=="3"~"C", HSD010=="4"~"D",HSD010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
DIQ160_char = case_when(DIQ160 == "1" ~ "yes", DIQ160 == "2" ~ "No", DIQ160 == "9" ~ "refused"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No", MCQ080 == "9" ~ "refused"),
MCQ160F_char=case_when(MCQ160F == "1" ~ "yes",MCQ160F== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"))
#Weight
design.INT9 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod5)
#Crosstab
design.INT9 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(RIDRETH1_char,DMDEDUC2_char,DMDMARTL_char,HSD010_char,SLQ050_char,DIQ160_char,MCQ010_char, MCQ080_char, MCQ160F_char, MCQ220_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status") %>%
bold_labels()
#Unadjusted OR
fit.ex53 <- svyglm(CVD~RIDRETH1_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex53)),exp(confint(fit.ex53,df.resid=degf(fit.ex53$survey.design))))[-1,]
summary(fit.ex53, df.resid=degf(fit.ex53$survey.design))
fit.ex54 <- svyglm(CVD~DMDMARTL_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex54)),exp(confint(fit.ex54,df.resid=degf(fit.ex54$survey.design))))[-1,]
summary(fit.ex54, df.resid=degf(fit.ex54$survey.design))
fit.ex55 <- svyglm(CVD~HSD010_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex55)),exp(confint(fit.ex55,df.resid=degf(fit.ex55$survey.design))))[-1,]
summary(fit.ex55, df.resid=degf(fit.ex55$survey.design))
fit.ex56 <- svyglm(CVD~SLQ050_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex56)),exp(confint(fit.ex56,df.resid=degf(fit.ex55$survey.design))))[-1,]
summary(fit.ex56, df.resid=degf(fit.ex56$survey.design))
fit.ex57 <- svyglm(CVD~DIQ160_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex57)),exp(confint(fit.ex57,df.resid=degf(fit.ex57$survey.design))))[-1,]
summary(fit.ex57, df.resid=degf(fit.ex57$survey.design))
fit.ex58 <- svyglm(CVD~RHQ291,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex58)),exp(confint(fit.ex58,df.resid=degf(fit.ex58$survey.design))))[-1,]
summary(fit.ex58, df.resid=degf(fit.ex58$survey.design))
fit.ex59 <- svyglm(CVD~MCQ010_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex59)),exp(confint(fit.ex59,df.resid=degf(fit.ex59$survey.design))))[-1,]
summary(fit.ex59, df.resid=degf(fit.ex59$survey.design))
fit.ex60 <- svyglm(CVD~MCQ080_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex60)),exp(confint(fit.ex60,df.resid=degf(fit.ex60$survey.design))))[-1,]
summary(fit.ex60, df.resid=degf(fit.ex60$survey.design))
fit.ex61 <- svyglm(CVD~MCQ160F_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex61)),exp(confint(fit.ex61,df.resid=degf(fit.ex61$survey.design))))[-1,]
summary(fit.ex61, df.resid=degf(fit.ex61$survey.design))
fit.ex62 <- svyglm(CVD~MCQ220_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex62)),exp(confint(fit.ex62,df.resid=degf(fit.ex62$survey.design))))[-1,]
summary(fit.ex62, df.resid=degf(fit.ex62$survey.design))
#Adjuted by demo
fit.ex63 <- svyglm(CVD~RIDAGEYR+DMDEDUC2_char+DMDMARTL_char+HSD010_char+SLQ050_char+DIQ160_char+RHQ291+ MCQ010_char+ MCQ080_char+ MCQ160F_char+ MCQ220_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex63)),exp(confint(fit.ex63,df.resid=degf(fit.ex63$survey.design))))[-1,]
summary(fit.ex63, df.resid=degf(fit.ex63$survey.design))
fit.ex63 <- svyglm(CVD~RIDAGEYR+DMDEDUC2_char+DMDMARTL_char+DIQ160_char+RHQ291+ MCQ010_char+ MCQ080_char+ MCQ160F_char+ MCQ220_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex63)),exp(confint(fit.ex63,df.resid=degf(fit.ex63$survey.design))))[-1,]
summary(fit.ex63, df.resid=degf(fit.ex63$survey.design))
fit.ex63 <- svyglm(CVD~RIDAGEYR+DMDEDUC2_char+DMDMARTL_char+DIQ160_char+ MCQ010_char+ MCQ080_char+ MCQ160F_char+ MCQ220_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex63)),exp(confint(fit.ex63,df.resid=degf(fit.ex63$survey.design))))[-1,]
summary(fit.ex63, df.resid=degf(fit.ex63$survey.design))
fit.ex63 <- svyglm(CVD~DMDEDUC2_char+DMDMARTL_char+DIQ160_char+ MCQ010_char+ MCQ080_char+ MCQ160F_char+ MCQ220_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex63)),exp(confint(fit.ex63,df.resid=degf(fit.ex63$survey.design))))[-1,]
summary(fit.ex63, df.resid=degf(fit.ex63$survey.design))
# Predicting for overweight
fit.ex64 <- svyglm(MCQ080~RIDRETH1_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex64)),exp(confint(fit.ex64,df.resid=degf(fit.ex64$survey.design))))[-1,]
summary(fit.ex64, df.resid=degf(fit.ex64$survey.design))
fit.ex65 <- svyglm(MCQ080~DMDMARTL_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex65)),exp(confint(fit.ex65,df.resid=degf(fit.ex65$survey.design))))[-1,]
summary(fit.ex65, df.resid=degf(fit.ex65$survey.design))
fit.ex66 <- svyglm(MCQ080~HSD010_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex66)),exp(confint(fit.ex66,df.resid=degf(fit.ex55$survey.design))))[-1,]
summary(fit.ex66, df.resid=degf(fit.ex66$survey.design))
fit.ex67 <- svyglm(MCQ080~SLQ050_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex67)),exp(confint(fit.ex67,df.resid=degf(fit.ex67$survey.design))))[-1,]
summary(fit.ex67, df.resid=degf(fit.ex67$survey.design))
fit.ex68 <- svyglm(MCQ080~DIQ160_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex68)),exp(confint(fit.ex68,df.resid=degf(fit.ex68$survey.design))))[-1,]
summary(fit.ex68, df.resid=degf(fit.ex68$survey.design))
fit.ex69 <- svyglm(MCQ080~RHQ291,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex69)),exp(confint(fit.ex69,df.resid=degf(fit.ex69$survey.design))))[-1,]
summary(fit.ex69, df.resid=degf(fit.ex69$survey.design))
fit.ex70 <- svyglm(MCQ080~MCQ010_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex70)),exp(confint(fit.ex70,df.resid=degf(fit.ex70$survey.design))))[-1,]
summary(fit.ex70, df.resid=degf(fit.ex70$survey.design))
fit.ex71 <- svyglm(MCQ080~CVD_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex71)),exp(confint(fit.ex71,df.resid=degf(fit.ex71$survey.design))))[-1,]
summary(fit.ex71, df.resid=degf(fit.ex71$survey.design))
fit.ex72 <- svyglm(MCQ080~MCQ160F_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex72)),exp(confint(fit.ex72,df.resid=degf(fit.ex72$survey.design))))[-1,]
summary(fit.ex72, df.resid=degf(fit.ex72$survey.design))
fit.ex73 <- svyglm(MCQ080~MCQ220_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex73)),exp(confint(fit.ex73,df.resid=degf(fit.ex73$survey.design))))[-1,]
summary(fit.ex73, df.resid=degf(fit.ex73$survey.design))
#Adjuted by demo
fit.ex74 <- svyglm(MCQ080~RIDAGEYR+DMDEDUC2_char+DMDMARTL_char+HSD010_char+SLQ050_char+DIQ160_char+RHQ291+ MCQ010_char+ CVD_char+ MCQ160F_char+ MCQ220_char,family=gaussian(),design=design.INT5)
cbind(exp(coef(fit.ex74)),exp(confint(fit.ex74,df.resid=degf(fit.ex74$survey.design))))[-1,]
summary(fit.ex74, df.resid=degf(fit.ex74$survey.design))
#95% ci for prevalence
svyby(~CVD_char,~RIDRETH1_char, design.INT9, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.INT9, svymean),
df = degf(design.INT9))
confint(svyby(~CVD_char,~DMDEDUC2_char, design.INT9, svymean),
df = degf(design.INT9))
confint(svyby(~CVD_char,~DMDMARTL_char, design.INT9, svymean),
df = degf(design.INT9))
confint(svyby(~CVD_char,~HSD010_char, design.INT9, svymean),
df = degf(design.INT9))
confint(svyby(~CVD_char,~SLQ050_char, design.INT9, svymean),
df = degf(design.INT9))
confint(svyby(~CVD_char,~DIQ160_char, design.INT9, svymean),
df = degf(design.INT9))
confint(svyby(~CVD_char,~MCQ010_char, design.INT9, svymean),
df = degf(design.INT9))
confint(svyby(~CVD_char,~MCQ080_char, design.INT9, svymean),
df = degf(design.INT9))
confint(svyby(~CVD_char,~MCQ160F_char, design.INT9, svymean),
df = degf(design.INT9))
confint(svyby(~CVD_char,~MCQ220_char, design.INT9, svymean),
df = degf(design.INT9))
========================================================================
#2017-2020
nhanes.mod8<- X2017_2020 %>% mutate(
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " , DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree", DMDEDUC2=="5"~"College graduate or above"),
DMDMARTZ_char=case_when(DMDMARTZ=="1"~"Married/Living with Partner" , DMDMARTZ=="2"~"Widowed/Divorced/Separated" , DMDMARTZ=="3"~ "Never married"),
HUQ010_char=case_when(HUQ010=="1"~"A",HUQ010=="2"~"B",HUQ010=="3"~"C", HUQ010=="4"~"D",HUQ010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
DIQ160_char = case_when(DIQ160 == "1" ~ "yes", DIQ160 == "2" ~ "No", DIQ160 == "9" ~ "refused"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No", MCQ080 == "9" ~ "refused"),
MCQ160F_char=case_when(MCQ160F == "1" ~ "yes",MCQ160F== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"))
#WEIGHT
design.INT8 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC3.2Y,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod8)
#95% ci for prevalence
svyby(~CVD_char,~RIDRETH1_char, design.INT8, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.INT8, svymean),
df = degf(design.INT8))
confint(svyby(~CVD_char,~DMDEDUC2_char, design.INT8, svymean),
df = degf(design.INT8))
confint(svyby(~CVD_char,~DMDMARTZ_char, design.INT8, svymean),
df = degf(design.INT8))
confint(svyby(~CVD_char,~HUQ010_char, design.INT8, svymean),
df = degf(design.INT8))
confint(svyby(~CVD_char,~SLQ050_char, design.INT8, svymean),
df = degf(design.INT8))
confint(svyby(~CVD_char,~DIQ160_char, design.INT8, svymean),
df = degf(design.INT8))
confint(svyby(~CVD_char,~MCQ010_char, design.INT8, svymean),
df = degf(design.INT8))
confint(svyby(~CVD_char,~MCQ080_char, design.INT8, svymean),
df = degf(design.INT8))
confint(svyby(~CVD_char,~MCQ160F_char, design.INT8, svymean),
df = degf(design.INT8))
confint(svyby(~CVD_char,~MCQ220_char, design.INT8, svymean),
df = degf(design.INT8))
#Unadjusted
fit.ex81 <- svyglm(CVD~RIDRETH1_char,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex81)),exp(confint(fit.ex81,df.resid=degf(fit.ex81$survey.design))))[-1,]
summary(fit.ex81, df.resid=degf(fit.ex81$survey.design))
fit.ex82 <- svyglm(CVD~DMDMARTZ_char,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex82)),exp(confint(fit.ex82,df.resid=degf(fit.ex82$survey.design))))[-1,]
summary(fit.ex82, df.resid=degf(fit.ex82$survey.design))
fit.ex83 <- svyglm(CVD~HUQ010_char,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex83)),exp(confint(fit.ex83,df.resid=degf(fit.ex83$survey.design))))[-1,]
summary(fit.ex83, df.resid=degf(fit.ex83$survey.design))
fit.ex84 <- svyglm(CVD~SLQ050_char,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex84)),exp(confint(fit.ex84,df.resid=degf(fit.ex584$survey.design))))[-1,]
summary(fit.ex84, df.resid=degf(fit.ex56$survey.design))
fit.ex85 <- svyglm(CVD~DIQ160_char,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex85)),exp(confint(fit.ex85,df.resid=degf(fit.ex85$survey.design))))[-1,]
summary(fit.ex85, df.resid=degf(fit.ex85$survey.design))
fit.ex86 <- svyglm(CVD~RHQ291,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex86)),exp(confint(fit.ex86,df.resid=degf(fit.ex86$survey.design))))[-1,]
summary(fit.ex86, df.resid=degf(fit.ex86$survey.design))
fit.ex87 <- svyglm(CVD~MCQ010_char,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex87)),exp(confint(fit.ex87,df.resid=degf(fit.ex87$survey.design))))[-1,]
summary(fit.ex87, df.resid=degf(fit.ex87$survey.design))
fit.ex88 <- svyglm(CVD~MCQ080_char,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex88)),exp(confint(fit.ex88,df.resid=degf(fit.ex88$survey.design))))[-1,]
summary(fit.ex88, df.resid=degf(fit.ex88$survey.design))
fit.ex89 <- svyglm(CVD~MCQ160F_char,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex89)),exp(confint(fit.ex89,df.resid=degf(fit.ex89$survey.design))))[-1,]
summary(fit.ex89, df.resid=degf(fit.ex89$survey.design))
fit.ex90 <- svyglm(CVD~MCQ220_char,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex90)),exp(confint(fit.ex90,df.resid=degf(fit.ex90$survey.design))))[-1,]
summary(fit.ex90, df.resid=degf(fit.ex90$survey.design))
#Crosstab
design.INT8 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(RIDRETH1_char,DMDEDUC2_char,DMDMARTZ_char,HUQ010_char,SLQ050_char,DIQ160_char,MCQ010_char, MCQ080_char, MCQ160F_char, MCQ220_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status") %>%
bold_labels()
#Subgropup for 2017-2020
nhanes.mod8<- X2017_2020 %>% mutate(RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " , DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree", DMDEDUC2=="5"~"College graduate or above"),
DMDMARTZ_char=case_when(DMDMARTZ=="1"~"Married/Living with Partner" , DMDMARTZ=="2"~"Widowed/Divorced/Separated" , DMDMARTZ=="3"~ "Never married"),
HUQ010_char=case_when(HUQ010=="1"~"A",HUQ010=="2"~"B",HUQ010=="3"~"C", HUQ010=="4"~"D",HUQ010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
DIQ160_char = case_when(DIQ160 == "1" ~ "yes", DIQ160 == "2" ~ "No", DIQ160 == "9" ~ "refused"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No", MCQ080 == "9" ~ "refused"),
MCQ160F_char=case_when(MCQ160F == "1" ~ "yes",MCQ160F== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"))
nhanes.mod112 <- nhanes.mod8 %>%
mutate(RIDRETH1_char == "black", RIDRETH1 == "4")
nhanes.mod112 <-nhanes.mod8 %>% ## select the columns of interest
filter(RIDRETH1 == "4")
#WEIGHT
design.INT81<- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC3.2Y,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod112)
#subset
design.FST.domain <- subset(design.INT81)
nwtco$incc2<-as.logical(with(nwtco, ifelse(rel | instit==2,1,rbinom(nrow(nwtco),1,.1))))
#Unadjusted
fit.ex81 <- svyglm(CVD~RIDRETH1_char,family=gaussian(),design=design.INT81)
cbind(exp(coef(fit.ex81)),exp(confint(fit.ex81,df.resid=degf(fit.ex81$survey.design))))[-1,]
summary(fit.ex81, df.resid=degf(fit.ex81$survey.design))
fit.ex82 <- svyglm(CVD~DMDMARTZ_char,family=gaussian(),design=design.INT81)
cbind(exp(coef(fit.ex82)),exp(confint(fit.ex82,df.resid=degf(fit.ex82$survey.design))))[-1,]
summary(fit.ex82, df.resid=degf(fit.ex82$survey.design))
fit.ex83 <- svyglm(CVD~HUQ010_char,family=gaussian(),design=design.INT81)
cbind(exp(coef(fit.ex83)),exp(confint(fit.ex83,df.resid=degf(fit.ex83$survey.design))))[-1,]
summary(fit.ex83, df.resid=degf(fit.ex83$survey.design))
fit.ex84 <- svyglm(CVD~SLQ050_char,family=gaussian(),design=design.INT81)
cbind(exp(coef(fit.ex84)),exp(confint(fit.ex84,df.resid=degf(fit.ex584$survey.design))))[-1,]
summary(fit.ex84, df.resid=degf(fit.ex56$survey.design))
fit.ex85 <- svyglm(CVD~DIQ160_char,family=gaussian(),design=design.INT81)
cbind(exp(coef(fit.ex85)),exp(confint(fit.ex85,df.resid=degf(fit.ex85$survey.design))))[-1,]
summary(fit.ex85, df.resid=degf(fit.ex85$survey.design))
fit.ex86 <- svyglm(CVD~RHQ291,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex86)),exp(confint(fit.ex86,df.resid=degf(fit.ex86$survey.design))))[-1,]
summary(fit.ex86, df.resid=degf(fit.ex86$survey.design))
fit.ex87 <- svyglm(CVD~MCQ010_char,family=gaussian(),design=design.INT81)
cbind(exp(coef(fit.ex87)),exp(confint(fit.ex87,df.resid=degf(fit.ex87$survey.design))))[-1,]
summary(fit.ex87, df.resid=degf(fit.ex87$survey.design))
fit.ex88 <- svyglm(CVD~MCQ080_char,family=gaussian(),design=design.INT81)
cbind(exp(coef(fit.ex88)),exp(confint(fit.ex88,df.resid=degf(fit.ex88$survey.design))))[-1,]
summary(fit.ex88, df.resid=degf(fit.ex88$survey.design))
fit.ex89 <- svyglm(CVD~MCQ160F_char,family=gaussian(),design=design.INT8)
cbind(exp(coef(fit.ex89)),exp(confint(fit.ex89,df.resid=degf(fit.ex89$survey.design))))[-1,]
summary(fit.ex89, df.resid=degf(fit.ex89$survey.design))
fit.ex90 <- svyglm(CVD~MCQ220_char,family=gaussian(),design=design.INT81)
cbind(exp(coef(fit.ex90)),exp(confint(fit.ex90,df.resid=degf(fit.ex90$survey.design))))[-1,]
summary(fit.ex90, df.resid=degf(fit.ex90$survey.design))
#95% ci for prevalence
svyby(~CVD_char,~RIDRETH1_char, design.INT81, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.INT81, svymean),
df = degf(design.INT81))
confint(svyby(~CVD_char,~DMDEDUC2_char, design.INT81, svymean),
df = degf(design.INT81))
confint(svyby(~CVD_char,~DMDMARTL_char, design.INT81, svymean),
df = degf(design.INT81))
confint(svyby(~CVD_char,~HSD010_char, design.INT81, svymean),
df = degf(design.INT81))
confint(svyby(~CVD_char,~SLQ050_char, design.INT81, svymean),
df = degf(design.INT81))
confint(svyby(~CVD_char,~DIQ160_char, design.INT81, svymean),
df = degf(design.INT81))
confint(svyby(~CVD_char,~MCQ010_char, design.INT81, svymean),
df = degf(design.INT81))
confint(svyby(~CVD_char,~MCQ080_char, design.INT81, svymean),
df = degf(design.INT81))
confint(svyby(~CVD_char,~MCQ160F_char, design.INT81, svymean),
df = degf(design.INT81))
confint(svyby(~CVD_char,~MCQ220_char, design.INT81, svymean),
df = degf(design.INT81))
#Crosstab
nhanes.mod112 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(RIDRETH1_char,DMDEDUC2_char,DMDMARTZ_char,HUQ010_char,SLQ050_char,DIQ160_char,MCQ010_char, MCQ080_char, MCQ160F_char, MCQ220_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status") %>%
bold_labels()
#mixed-------------working
nrow(X2017_2020)
nhanes.mod120 <- X2017_2020 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DMDMARTZ_char=case_when(DMDMARTZ=="1"~"Married/Living with Partner" ,
DMDMARTZ=="2"~"Widowed/Divorced/Separated" , DMDMARTZ=="3"~ "Never married"),
HUQ010_char=case_when(HUQ010=="1"~"A",HUQ010=="2"~"B",HUQ010=="3"~"C", HUQ010=="4"~"D",
HUQ010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
DIQ160_char = case_when(DIQ160 == "1" ~ "yes", DIQ160 == "2" ~ "No", DIQ160 == "9" ~ "refused"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No", MCQ080 == "9" ~ "refused"),
MCQ160F_char=case_when(MCQ160F == "1" ~ "yes",MCQ160F== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC3.2Y = case_when( is.na(WTMEC3.2Y) ~ 0,
!is.na(WTMEC3.2Y) ~ as.numeric(WTMEC3.2Y)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(HUQ010) &!is.na(DMDMARTZ) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(CVD) &
!is.na(DMDEDUC2) & !is.na(SLQ050) & !is.na(DIQ160) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160F) & !is.na(MCQ220) & WTMEC3.2Y > 0)
nhanes.mod121 <- nhanes.mod120 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST121 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC3.2Y,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod121)
design.FST.domain121 <- subset(design.FST121, domain)
library(gtsummary)
design.FST.domain121 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(RIDAGEYR, RIDRETH1_char,DMDEDUC2_char,DMDMARTZ_char,HUQ010_char,SLQ050_char,
DIQ160_char,MCQ010_char, MCQ080_char, MCQ160F_char, MCQ220_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status\n
(Females age 45y and older)") %>%
bold_labels()
#Frequency Recheck
prop.table(svytable(~CVD_char + SLQ050_char, design.FST.domain121), margin = 2)
design.FST.domain121 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(MCQ160F_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status\n
(Females age 45y and older)") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain121)
confint(svymean(~RIDAGEYR, design.FST.domain121), df = degf(design.FST.domain121))
#final prevalence and CI
#95% ci for prevalence
svyby(~CVD_char,~RIDRETH1, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~I(CVD=="1"),~DMDEDUC2, design.FST.domain121, svymean)
confint(svyby(I(CVD=="1"),~DMDEDUC2, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char+DMDMARTZ_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char+DMDMARTZ_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD,~HUQ010, design.FST.domain121, svymean)
confint(svyby(~CVD,~HUQ010, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~SLQ050_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~SLQ050_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~DIQ160_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~DIQ160_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~MCQ010_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~MCQ010_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~MCQ080_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~MCQ080_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~MCQ160F_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~MCQ160F_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~MCQ220_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~MCQ220_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~DMDEDUC2, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~DMDEDUC2, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~DMDMARTL, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~DMDMARTL, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~HUQ010, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~HUQ010, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~SLQ050, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~SLQ050, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD,~DMDEDUC2_char, design.FST.domain121, svymean)
confint(svyby(~CVD,~DMDEDUC2_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD,~DMDMARTZ_char, design.FST.domain121, svymean)
confint(svyby(~CVD,~DMDMARTZ_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD,~HUQ010_char, design.FST.domain121, svymean)
confint(svyby(~CVD,~HUQ010_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~DMDMARTZ_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~DMDMARTZ_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~DMDMARTZ_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~DMDMARTZ_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char, ~DMDMARTZ_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~DMDMARTZ_char, design.FST121, svymean),
df = degf(design.FST121))
#Weighted regression 2017_2020
fit.ex9.1.domain121 <- svyglm(CVD ~ RIDRETH1_char+DMDEDUC2_char+DMDMARTZ_char+
HUQ010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+ MCQ160F_char+
MCQ220_char,
family=gaussian(),
design=design.FST.domain121)
fit.ex9.1.domain121 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTZ_char+
HUQ010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char,
family=gaussian(),
design=design.FST.domain121)
fit.ex9.1.domain121 %>%
tbl_regression(intercept = T,
estimate_fun = function(x) style_sigfig(x, digits = 3),
pvalue_fun = function(x) style_pvalue(x, digits = 3),
label = list(RIDRETH1~"Races"
DMDEDUC2_char ~ "Education",
DMDMARTZ_char ~ "Marital",
HUQ010_char ~ "General Health",
SLQ050_char ~ "Sleep",
DIQ160_char ~ "prediabetes",
MCQ010_char ~ "Asthma",
MCQ080_char ~ "overweight",
MCQ160F_char ~ "stroke",
MCQ220_char ~ "cancer")) %>%
add_global_p(keep = T, test.statistic = "F") %>%
modify_caption("Weighted linear regression results for fasting glucose (mmol/L)\n
(Females age 45y and older)")
fit.ex121 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTZ_char+
HUQ010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ RIDRETH1, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ DMDEDUC2_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ DMDMARTZ_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ HUQ010_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ SLQ050_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ DIQ160_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ MCQ010_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ MCQ080_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ MCQ160F_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ MCQ220_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex121 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTZ_char, family=quasibinomial(),
design = design.FST.domain121)
summary(fit.ex121, df.resid=degf(fit.ex121$survey.design))
confint(fit.ex121, ddf.resid=degf(fit.ex121$survey.design))
OR.CI <- cbind(exp( coef(fit.ex121)),
exp(confint(fit.ex121,
df.resid=degf(fit.ex121$survey.design))))[-1,]
round(OR.CI, 3)
# cORRECTED 2013-2014 working
nrow(X2013_14)
nhanes.mod130 <- X2013_14 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married/Living with Partner" ,
DMDMARTL=="2"~"Widowed/Divorced/Separated" , DMDMARTL=="3"~ "Never married"),
HSD010_char=case_when(HSD010=="1"~"A",HSD010=="2"~"B",HSD010=="3"~"C", HSD010=="4"~"D",
HSD010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
DIQ160_char = case_when(DIQ160 == "1" ~ "yes", DIQ160 == "2" ~ "No", DIQ160 == "9" ~ "refused"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No", MCQ080 == "9" ~ "refused"),
MCQ160F_char=case_when(MCQ160F == "1" ~ "yes",MCQ160F== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD1_char=case_when(CVD1 == "0" ~ "NO", CVD1== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(HSD010) &!is.na(DMDMARTL) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(CVD1) &
!is.na(DMDEDUC2) & !is.na(SLQ050) & !is.na(DIQ160) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160F) & !is.na(MCQ220) & WTMEC2YR > 0)
nhanes.mod131 <- nhanes.mod130 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST131 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod131)
design.FST.domain131 <- subset(design.FST131, domain)
library(gtsummary)
design.FST.domain131 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD1_char,
# Use include to select variables
include = c(RIDAGEYR, RIDRETH1_char,DMDEDUC2_char,DMDMARTL_char,HSD010_char,SLQ050_char,
DIQ160_char,MCQ010_char, MCQ080_char, MCQ160F_char, MCQ220_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status\n
(Females age 45y and older)") %>%
bold_labels()
fit.ex9.1.domain131 <- svyglm(CVD1 ~ RIDRETH1_char+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+ MCQ160F_char+
MCQ220_char,
family=gaussian(),
design=design.FST.domain131)
fit.ex9.1.domain131 <- svyglm(CVD1 ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char,
family=gaussian(),
design=design.FST.domain131)
fit.ex9.1.domain131 %>%
tbl_regression(intercept = T,
estimate_fun = function(x) style_sigfig(x, digits = 3),
pvalue_fun = function(x) style_pvalue(x, digits = 3),
label = list(RIDRETH1~"Races"
DMDEDUC2_char ~ "Education",
DMDMARTZ_char ~ "Marital",
HUQ010_char ~ "General Health",
SLQ050_char ~ "Sleep",
DIQ160_char ~ "prediabetes",
MCQ010_char ~ "Asthma",
MCQ080_char ~ "overweight",
MCQ160F_char ~ "stroke",
MCQ220_char ~ "cancer")) %>%
add_global_p(keep = T, test.statistic = "F") %>%
modify_caption("Weighted linear regression results for fasting glucose (mmol/L)\n
(Females age 45y and older)")
fit.ex131 <- svyglm(CVD1 ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex131, df.resid=degf(fit.ex131$survey.design))
confint(fit.ex131, ddf.resid=degf(fit.ex131$survey.design))
OR.CI <- cbind(exp( coef(fit.ex131)),
exp(confint(fit.ex131,
df.resid=degf(fit.ex131$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex132 <- svyglm(CVD1 ~ RIDRETH1, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex132, df.resid=degf(fit.ex132$survey.design))
confint(fit.ex132, ddf.resid=degf(fit.ex132$survey.design))
OR.CI <- cbind(exp( coef(fit.ex132)),
exp(confint(fit.ex132,
df.resid=degf(fit.ex132$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex133 <- svyglm(CVD1 ~ DMDEDUC2_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex133, df.resid=degf(fit.ex133$survey.design))
confint(fit.ex133, ddf.resid=degf(fit.ex133$survey.design))
OR.CI <- cbind(exp( coef(fit.ex133)),
exp(confint(fit.ex133,
df.resid=degf(fit.ex133$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex134 <- svyglm(CVD1 ~ DMDMARTL_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex134, df.resid=degf(fit.ex134$survey.design))
confint(fit.ex134, ddf.resid=degf(fit.ex341$survey.design))
OR.CI <- cbind(exp( coef(fit.ex134)),
exp(confint(fit.ex134,
df.resid=degf(fit.ex134$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex135 <- svyglm(CVD1 ~
HSD010_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex135, df.resid=degf(fit.ex135$survey.design))
confint(fit.ex135, ddf.resid=degf(fit.ex135$survey.design))
OR.CI <- cbind(exp( coef(fit.ex135)),
exp(confint(fit.ex135,
df.resid=degf(fit.ex134$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex136 <- svyglm(CVD1 ~ SLQ050_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex136, df.resid=degf(fit.ex136$survey.design))
confint(fit.ex136, ddf.resid=degf(fit.ex136$survey.design))
OR.CI <- cbind(exp( coef(fit.ex136)),
exp(confint(fit.ex136,
df.resid=degf(fit.ex136$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex137 <- svyglm(CVD1 ~ DIQ160_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex137, df.resid=degf(fit.ex137$survey.design))
confint(fit.ex137, ddf.resid=degf(fit.ex137$survey.design))
OR.CI <- cbind(exp( coef(fit.ex137)),
exp(confint(fit.ex137,
df.resid=degf(fit.ex137$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex138 <- svyglm(CVD1 ~ MCQ010_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex138, df.resid=degf(fit.ex138$survey.design))
confint(fit.ex138, ddf.resid=degf(fit.ex138$survey.design))
OR.CI <- cbind(exp( coef(fit.ex138)),
exp(confint(fit.ex138,
df.resid=degf(fit.ex138$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex139 <- svyglm(CVD1 ~ MCQ080_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex139, df.resid=degf(fit.ex139$survey.design))
confint(fit.ex139, ddf.resid=degf(fit.ex139$survey.design))
OR.CI <- cbind(exp( coef(fit.ex139)),
exp(confint(fit.ex139,
df.resid=degf(fit.ex139$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex141 <- svyglm(CVD1 ~ MCQ160F_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex141, df.resid=degf(fit.ex141$survey.design))
confint(fit.ex141, ddf.resid=degf(fit.ex141$survey.design))
OR.CI <- cbind(exp( coef(fit.ex141)),
exp(confint(fit.ex141,
df.resid=degf(fit.ex141$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex142 <- svyglm(CVD1 ~ MCQ220_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex142, df.resid=degf(fit.ex142$survey.design))
confint(fit.ex142, ddf.resid=degf(fit.ex142$survey.design))
OR.CI <- cbind(exp( coef(fit.ex142)),
exp(confint(fit.ex142,
df.resid=degf(fit.ex142$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex143 <- svyglm(CVD1 ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex143, df.resid=degf(fit.ex143$survey.design))
confint(fit.ex143, ddf.resid=degf(fit.ex143$survey.design))
OR.CI <- cbind(exp( coef(fit.ex143)),
exp(confint(fit.ex143,
df.resid=degf(fit.ex143$survey.design))))[-1,]
round(OR.CI, 3)
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain131)
confint(svymean(~RIDAGEYR, design.FST.domain131), df = degf(design.FST.domain131))
#95% ci for prevalence
svyby(~CVD1_char,~RIDRETH1, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1,~DMDEDUC2, design.FST.domain131, svymean)
confint(svyby(~CVD,~DMDEDUC2, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1,~DMDMARTL, design.FST.domain131, svymean)
confint(svyby(~CVD,~DMDMARTL, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1,~HSD010, design.FST.domain131, svymean)
confint(svyby(~CVD,~HSD010, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~SLQ050_char, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~SLQ050_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~DIQ160_char, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~DIQ160_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~MCQ010_char, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~MCQ010_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~MCQ080_char, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~MCQ080_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~MCQ160F_char, design.FST.domain131, svymean)
confint(svyby(~CVD1_char,~MCQ160F_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~MCQ220_char, design.FST.domain131, svymean)
confint(svyby(~CVD1_char,~MCQ220_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~DMDEDUC2, design.FST.domain131, svymean)
confint(svyby(~CVD1_char,~DMDEDUC2, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~DMDMARTL, design.FST.domain131, svymean)
confint(svyby(~CVD1_char,~DMDMARTL, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~HSD010, design.FST.domain131, svymean)
confint(svyby(~CVD1_char,~HSD010, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~SLQ050, design.FST.domain131, svymean)
confint(svyby(~CVD1_char,~SLQ050, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1,~DMDEDUC2_char, design.FST.domain131, svymean)
confint(svyby(~CVD1,~DMDEDUC2_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
confint(svyby(~CVD1,~DMDMARTL_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
confint(svyby(~CVD1,~HSD010_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
confint(svyby(~CVD1_char,~DMDMARTL_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
confint(svyby(~CVD1_char,~DMDMARTL_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
confint(svyby(~CVD1_char, ~DMDMARTL_char, design.FST.domain131, svymean))
confint(svyby(~CVD1_char,~DMDMARTL_char, design.FST131, svymean),
df = degf(design.FST131))
# For 2015-2016
nhanes.mod160 <- X2015_16 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married/Living with Partner" ,
DMDMARTL=="2"~"Widowed/Divorced/Separated" , DMDMARTL=="3"~ "Never married"),
HSD010_char=case_when(HSD010=="1"~"A",HSD010=="2"~"B",HSD010=="3"~"C", HSD010=="4"~"D",
HSD010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
DIQ160_char = case_when(DIQ160 == "1" ~ "yes", DIQ160 == "2" ~ "No", DIQ160 == "9" ~ "refused"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No", MCQ080 == "9" ~ "refused"),
MCQ160F_char=case_when(MCQ160F == "1" ~ "yes",MCQ160F== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(HSD010) &!is.na(DMDMARTL) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(CVD) &
!is.na(DMDEDUC2) & !is.na(SLQ050) & !is.na(DIQ160) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160F) & !is.na(MCQ220) & WTMEC2YR > 0)
nhanes.mod161 <- nhanes.mod160 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST161 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod161)
design.FST.domain161 <- subset(design.FST161, domain)
library(gtsummary)
design.FST.domain161 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(RIDAGEYR, RIDRETH1_char,DMDEDUC2_char,DMDMARTL_char,HSD010_char,SLQ050_char,
DIQ160_char,MCQ010_char, MCQ080_char, MCQ160F_char, MCQ220_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status\n
(Females age 45y and older)") %>%
bold_labels()
fit.ex9.1.domain161 <- svyglm(CVD ~ RIDRETH1_char+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+ MCQ160F_char+
MCQ220_char,
family=gaussian(),
design=design.FST.domain161)
fit.ex9.1.domain161 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char,
family=gaussian(),
design=design.FST.domain161)
fit.ex9.1.domain161 %>%
tbl_regression(intercept = T,
estimate_fun = function(x) style_sigfig(x, digits = 3),
pvalue_fun = function(x) style_pvalue(x, digits = 3),
label = list(RIDRETH1~"Races"
DMDEDUC2_char ~ "Education",
DMDMARTL_char ~ "Marital",
HSD010_char ~ "General Health",
SLQ050_char ~ "Sleep",
DIQ160_char ~ "prediabetes",
MCQ010_char ~ "Asthma",
MCQ080_char ~ "overweight",
MCQ160F_char ~ "stroke",
MCQ220_char ~ "cancer")) %>%
add_global_p(keep = T, test.statistic = "F") %>%
modify_caption("Weighted linear regression results for fasting glucose (mmol/L)\n
(Females age 45y and older)")
fit.ex161 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex161, df.resid=degf(fit.ex161$survey.design))
confint(fit.ex161, ddf.resid=degf(fit.ex161$survey.design))
OR.CI <- cbind(exp( coef(fit.ex161)),
exp(confint(fit.ex161,
df.resid=degf(fit.ex161$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex162 <- svyglm(CVD ~ RIDRETH1, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex162, df.resid=degf(fit.ex162$survey.design))
confint(fit.ex162, ddf.resid=degf(fit.ex162$survey.design))
OR.CI <- cbind(exp( coef(fit.ex162)),
exp(confint(fit.ex162,
df.resid=degf(fit.ex162$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex163 <- svyglm(CVD ~ DMDEDUC2_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex163, df.resid=degf(fit.ex163$survey.design))
confint(fit.ex163, ddf.resid=degf(fit.ex163$survey.design))
OR.CI <- cbind(exp( coef(fit.ex163)),
exp(confint(fit.ex163,
df.resid=degf(fit.ex163$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex164 <- svyglm(CVD ~ DMDMARTL_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex164, df.resid=degf(fit.ex164$survey.design))
confint(fit.ex164, ddf.resid=degf(fit.ex164$survey.design))
OR.CI <- cbind(exp( coef(fit.ex164)),
exp(confint(fit.ex164,
df.resid=degf(fit.ex164$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex165 <- svyglm(CVD ~
HSD010_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex165, df.resid=degf(fit.ex165$survey.design))
confint(fit.ex165, ddf.resid=degf(fit.ex165$survey.design))
OR.CI <- cbind(exp( coef(fit.ex165)),
exp(confint(fit.ex165,
df.resid=degf(fit.ex164$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex166 <- svyglm(CVD ~ SLQ050_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex166, df.resid=degf(fit.ex166$survey.design))
confint(fit.ex166, ddf.resid=degf(fit.ex166$survey.design))
OR.CI <- cbind(exp( coef(fit.ex166)),
exp(confint(fit.ex166,
df.resid=degf(fit.ex166$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex167 <- svyglm(CVD ~ DIQ160_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex167, df.resid=degf(fit.ex167$survey.design))
confint(fit.ex167, ddf.resid=degf(fit.ex167$survey.design))
OR.CI <- cbind(exp( coef(fit.ex167)),
exp(confint(fit.ex167,
df.resid=degf(fit.ex167$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex168 <- svyglm(CVD ~ MCQ010_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex168, df.resid=degf(fit.ex168$survey.design))
confint(fit.ex168, ddf.resid=degf(fit.ex168$survey.design))
OR.CI <- cbind(exp( coef(fit.ex168)),
exp(confint(fit.ex168,
df.resid=degf(fit.ex168$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex169 <- svyglm(CVD ~ MCQ080_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex169, df.resid=degf(fit.ex169$survey.design))
confint(fit.ex169, ddf.resid=degf(fit.ex169$survey.design))
OR.CI <- cbind(exp( coef(fit.ex169)),
exp(confint(fit.ex169,
df.resid=degf(fit.ex169$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex171 <- svyglm(CVD ~ MCQ160F_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex171, df.resid=degf(fit.ex171$survey.design))
confint(fit.ex171, ddf.resid=degf(fit.ex171$survey.design))
OR.CI <- cbind(exp( coef(fit.ex171)),
exp(confint(fit.ex171,
df.resid=degf(fit.ex171$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex172 <- svyglm(CVD ~ MCQ220_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex172, df.resid=degf(fit.ex172$survey.design))
confint(fit.ex172, ddf.resid=degf(fit.ex172$survey.design))
OR.CI <- cbind(exp( coef(fit.ex172)),
exp(confint(fit.ex172,
df.resid=degf(fit.ex172$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex173 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char, family=quasibinomial(),
design = design.FST.domain161)
summary(fit.ex173, df.resid=degf(fit.ex173$survey.design))
confint(fit.ex173, ddf.resid=degf(fit.ex173$survey.design))
OR.CI <- cbind(exp( coef(fit.ex173)),
exp(confint(fit.ex173,
df.resid=degf(fit.ex173$survey.design))))[-1,]
round(OR.CI, 3)
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain161)
confint(svymean(~RIDAGEYR, design.FST.domain161), df = degf(design.FST.domain161))
#95% ci for prevalence
svyby(~CVD_char,~RIDRETH1, design.FST.domain161, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~CVD,~DMDEDUC2, design.FST.domain161, svymean)
confint(svyby(~CVD,~DMDEDUC2, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~CVD,~DMDMARTL, design.FST.domain161, svymean)
confint(svyby(~CVD,~DMDMARTL, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~CVD,~HSD010, design.FST.domain161, svymean)
confint(svyby(~CVD,~HSD010, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~CVD_char,~SLQ050_char, design.FST.domain161, svymean)
confint(svyby(~CVD_char,~SLQ050_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~CVD_char,~DIQ160_char, design.FST.domain161, svymean)
confint(svyby(~CVD_char,~DIQ160_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~CVD_char,~MCQ010_char, design.FST.domain161, svymean)
confint(svyby(~CVD_char,~MCQ010_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~CVD_char,~MCQ080_char, design.FST.domain161, svymean)
confint(svyby(~CVD_char,~MCQ080_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~CVD_char,~MCQ160F_char, design.FST.domain161, svymean)
confint(svyby(~CVD_char,~MCQ160F_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~CVD_char,~MCQ220_char, design.FST.domain161, svymean)
confint(svyby(~CVD_char,~MCQ220_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
confint(svyby(~CVD_char,~DMDEDUC2, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
confint(svyby(~CVD_char,~DMDMARTL, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
confint(svyby(~CVD_char,~HSD010, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
confint(svyby(~CVD_char,~SLQ050, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
confint(svyby(~CVD,~DMDEDUC2_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
confint(svyby(~CVD,~DMDMARTL_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
confint(svyby(~CVD,~HSD010_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
confint(svyby(~CVD_char,~DMDMARTL_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
confint(svyby(~CVD_char,~DMDMARTL_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
confint(svyby(~CVD_char, ~DMDMARTL_char, design.FST.domain161, svymean))
confint(svyby(~CVD_char,~DMDMARTL_char, design.FST161, svymean),
df = degf(design.FST161))
#Regression visualization
#combined
nhanes.mod670 <- Combined_data_1 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married/Living with Partner" ,
DMDMARTL=="2"~"Widowed/Divorced/Separated" , DMDMARTL=="3"~ "Never married"),
HSD010_char=case_when(HSD010=="1"~"A",HSD010=="2"~"B",HSD010=="3"~"C", HSD010=="4"~"D",
HSD010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
DIQ160_char = case_when(DIQ160 == "1" ~ "yes", DIQ160 == "2" ~ "No", DIQ160 == "9" ~ "refused"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No", MCQ080 == "9" ~ "refused"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(HSD010) &!is.na(DMDMARTL) & !is.na(RIDRETH1) &
!is.na(CVD) &
!is.na(DMDEDUC2) & !is.na(SLQ050) & !is.na(DIQ160) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na(MCQ220) & WTMEC2YR > 0)
nhanes.mod671 <- nhanes.mod670 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST671 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod671)
design.FST.domain671 <- subset(design.FST671, domain)
library(gtsummary)
design.FST.domain671 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c( RIDRETH1_char,DMDEDUC2_char,DMDMARTL_char,HSD010_char,SLQ050_char,
DIQ160_char,MCQ010_char, MCQ080_char, MCQ220_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Sample Size and Weighted Sample
Characteristics by CVD Status Among Black African American with hysterectomy,
1999-2016") %>%
bold_labels()
fit.ex9.1.domain671 <- svyglm(CVD ~ RIDRETH1_char+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ220_char,
family=gaussian(),
design=design.FST.domain671)
fit.ex9.1.domain671 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ220_char,
family=gaussian(),
design=design.FST.domain671)
fit.ex9.1.domain671 %>%
tbl_regression(intercept = T,
estimate_fun = function(x) style_sigfig(x, digits = 3),
pvalue_fun = function(x) style_pvalue(x, digits = 3),
label = list(
DMDEDUC2_char ~ "Education",
DMDMARTL_char ~ "Marital",
HSD010_char ~ "General Health",
SLQ050_char ~ "Sleep",
DIQ160_char ~ "prediabetes",
MCQ010_char ~ "Asthma",
MCQ080_char ~ "overweight",
MCQ220_char ~ "cancer")) %>%
add_global_p(keep = T, test.statistic = "F") %>%
modify_caption("Weighted linear regression results for fasting glucose (mmol/L)\n
(Females age 45y and older)")
fit.ex671 <- svyglm(CVD ~ DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ220_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex671, df.resid=degf(fit.ex671$survey.design))
confint(fit.ex671, ddf.resid=degf(fit.ex671$survey.design))
OR.CI <- cbind(exp( coef(fit.ex671)),
exp(confint(fit.ex671,
df.resid=degf(fit.ex671$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex672 <- svyglm(CVD ~ RIDRETH1, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex672, df.resid=degf(fit.ex672$survey.design))
confint(fit.ex672, ddf.resid=degf(fit.ex672$survey.design))
OR.CI <- cbind(exp( coef(fit.ex672)),
exp(confint(fit.ex672,
df.resid=degf(fit.ex672$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex63 <- svyglm(CVD ~ DMDEDUC2_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex63, df.resid=degf(fit.ex63$survey.design))
confint(fit.ex63, ddf.resid=degf(fit.ex63$survey.design))
OR.CI <- cbind(exp( coef(fit.ex63)),
exp(confint(fit.ex63,
df.resid=degf(fit.ex63$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex64 <- svyglm(CVD ~ DMDMARTL_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex64, df.resid=degf(fit.ex64$survey.design))
confint(fit.ex64, ddf.resid=degf(fit.ex64$survey.design))
OR.CI <- cbind(exp( coef(fit.ex64)),
exp(confint(fit.ex64,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex65 <- svyglm(CVD ~
HSD010_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex65, df.resid=degf(fit.ex65$survey.design))
confint(fit.ex65, ddf.resid=degf(fit.ex65$survey.design))
OR.CI <- cbind(exp( coef(fit.ex65)),
exp(confint(fit.ex65,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex66 <- svyglm(CVD ~ SLQ050_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex66, df.resid=degf(fit.ex66$survey.design))
confint(fit.ex66, ddf.resid=degf(fit.ex66$survey.design))
OR.CI <- cbind(exp( coef(fit.ex66)),
exp(confint(fit.ex66,
df.resid=degf(fit.ex66$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex67 <- svyglm(CVD ~ DIQ160_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex67, df.resid=degf(fit.ex67$survey.design))
confint(fit.ex67, ddf.resid=degf(fit.ex67$survey.design))
OR.CI <- cbind(exp( coef(fit.ex67)),
exp(confint(fit.ex67,
df.resid=degf(fit.ex67$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex68 <- svyglm(CVD ~ MCQ010_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex68, df.resid=degf(fit.ex68$survey.design))
confint(fit.ex68, ddf.resid=degf(fit.ex68$survey.design))
OR.CI <- cbind(exp( coef(fit.ex68)),
exp(confint(fit.ex68,
df.resid=degf(fit.ex68$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex69 <- svyglm(CVD ~ MCQ080_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex69, df.resid=degf(fit.ex69$survey.design))
confint(fit.ex69, ddf.resid=degf(fit.ex69$survey.design))
OR.CI <- cbind(exp( coef(fit.ex69)),
exp(confint(fit.ex69,
df.resid=degf(fit.ex69$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex72 <- svyglm(CVD ~ MCQ220_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex72, df.resid=degf(fit.ex72$survey.design))
confint(fit.ex72, ddf.resid=degf(fit.ex72$survey.design))
OR.CI <- cbind(exp( coef(fit.ex72)),
exp(confint(fit.ex72,
df.resid=degf(fit.ex72$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex73 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex73, df.resid=degf(fit.ex73$survey.design))
confint(fit.ex73, ddf.resid=degf(fit.ex73$survey.design))
OR.CI <- cbind(exp( coef(fit.ex73)),
exp(confint(fit.ex73,
df.resid=degf(fit.ex73$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex73 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+
HSD010_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex73, df.resid=degf(fit.ex73$survey.design))
confint(fit.ex73, ddf.resid=degf(fit.ex73$survey.design))
OR.CI <- cbind(exp( coef(fit.ex73)),
exp(confint(fit.ex73,
df.resid=degf(fit.ex73$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex66 <- svyglm(CVD ~ SLQ050_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex66, df.resid=degf(fit.ex66$survey.design))
confint(fit.ex66, ddf.resid=degf(fit.ex66$survey.design))
OR.CI <- cbind(exp( coef(fit.ex66)),
exp(confint(fit.ex66,
df.resid=degf(fit.ex66$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex67 <- svyglm(CVD ~ DIQ160_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex67, df.resid=degf(fit.ex67$survey.design))
confint(fit.ex67, ddf.resid=degf(fit.ex67$survey.design))
OR.CI <- cbind(exp( coef(fit.ex67)),
exp(confint(fit.ex67,
df.resid=degf(fit.ex67$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex68 <- svyglm(CVD ~ MCQ010_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex68, df.resid=degf(fit.ex68$survey.design))
confint(fit.ex68, ddf.resid=degf(fit.ex68$survey.design))
OR.CI <- cbind(exp( coef(fit.ex68)),
exp(confint(fit.ex68,
df.resid=degf(fit.ex68$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex69 <- svyglm(CVD ~ MCQ080_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex69, df.resid=degf(fit.ex69$survey.design))
confint(fit.ex69, ddf.resid=degf(fit.ex69$survey.design))
OR.CI <- cbind(exp( coef(fit.ex69)),
exp(confint(fit.ex69,
df.resid=degf(fit.ex69$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex71 <- svyglm(CVD ~ MCQ160F_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex71, df.resid=degf(fit.ex71$survey.design))
confint(fit.ex71, ddf.resid=degf(fit.ex71$survey.design))
OR.CI <- cbind(exp( coef(fit.ex71)),
exp(confint(fit.ex71,
df.resid=degf(fit.ex71$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex72 <- svyglm(CVD ~ SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex72, df.resid=degf(fit.ex72$survey.design))
confint(fit.ex72, ddf.resid=degf(fit.ex72$survey.design))
OR.CI <- cbind(exp( coef(fit.ex72)),
exp(confint(fit.ex72,
df.resid=degf(fit.ex72$survey.design))))[-1,]
round(OR.CI, 3)
#vizulization practice
install.packages("ggplot2")
library(ggplot2)
# turn-off scientific notation like 1e+48
options(scipen=999)
theme_set(theme_bw())
data('design.FST.domain671' , package = "ggplot2")
k1<-ggplot(practice_vizualization, aes(x = Year, y = Sleep_problem, group = 1)) +
geom_line() +
geom_point()
k2<-ggplot(practice_vizualization, aes(x = Year, y = Asthma, group = 1)) +
geom_line() +
geom_point()
k3<-ggplot(practice_vizualization, aes(x = Year, y =Stroke, group = 1)) +
geom_line() +
geom_point()
k4<-ggplot(practice_vizualization, aes(x = Year, y = Cancer, group = 1)) +
geom_line() +
geom_point()
k5<-ggplot(practice_vizualization, aes(x = Year, y = Edu_less_9th_grade, group = 1)) +
geom_line() +
geom_point()
k6<-ggplot(practice_vizualization, aes(x = Year, y = Good_Health, group = 1)) +
geom_line() +
geom_point()
k7<-ggplot(practice_vizualization, aes(x = Year, y = Fair_Health, group = 1)) +
geom_line() +
geom_point()
install.packages(' gridExtra')
library( gridExtra)
ggarrange(k1, k2,k3,k4,k5,k6,k7, ncol = 2, nrow = 4)
install.packages("ggpubr")
library(ggpubr)
plot<- ggarrange(k1, k2,k3,k4,k5,k6,k7, ncol = 2, nrow = 4,
common.legend = TRUE,legend="bottom")
annotate_figure(plot, top = text_grob("Weighted Significant Trend prevalence(%) of CHD for Black African American women with hysterectomy those not having regular periods ",
color = "red", face = "bold", size = 14))
# 2013-2014 working
nrow(X2013_14)
nhanes.mod130 <- X2013_14 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married/Living with Partner" ,
DMDMARTL=="2"~"Widowed/Divorced/Separated" , DMDMARTL=="3"~ "Never married"),
HSD010_char=case_when(HSD010=="1"~"A",HSD010=="2"~"B",HSD010=="3"~"C", HSD010=="4"~"D",
HSD010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
DIQ160_char = case_when(DIQ160 == "1" ~ "yes", DIQ160 == "2" ~ "No", DIQ160 == "9" ~ "refused"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No", MCQ080 == "9" ~ "refused"),
MCQ160F_char=case_when(MCQ160F == "1" ~ "yes",MCQ160F== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(HSD010) &!is.na(DMDMARTL) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(CVD1) &
!is.na(DMDEDUC2) & !is.na(SLQ050) & !is.na(DIQ160) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160F) & !is.na(MCQ220) & WTMEC2YR > 0)
nhanes.mod131 <- nhanes.mod130 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST131 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod131)
design.FST.domain131 <- subset(design.FST131, domain)
library(gtsummary)
design.FST.domain131 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(RIDAGEYR, RIDRETH1_char,DMDEDUC2_char,DMDMARTL_char,HSD010_char,SLQ050_char,
DIQ160_char,MCQ010_char, MCQ080_char, MCQ160F_char, MCQ220_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status\n
(Females age 45y and older)") %>%
bold_labels()
fit.ex9.1.domain131 <- svyglm(CVD ~ RIDRETH1_char+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+ MCQ160F_char+
MCQ220_char,
family=gaussian(),
design=design.FST.domain131)
fit.ex9.1.domain131 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char,
family=gaussian(),
design=design.FST.domain131)
fit.ex9.1.domain131 %>%
tbl_regression(intercept = T,
estimate_fun = function(x) style_sigfig(x, digits = 3),
pvalue_fun = function(x) style_pvalue(x, digits = 3),
label = list(RIDRETH1~"Races"
DMDEDUC2_char ~ "Education",
DMDMARTZ_char ~ "Marital",
HUQ010_char ~ "General Health",
SLQ050_char ~ "Sleep",
DIQ160_char ~ "prediabetes",
MCQ010_char ~ "Asthma",
MCQ080_char ~ "overweight",
MCQ160F_char ~ "stroke",
MCQ220_char ~ "cancer")) %>%
add_global_p(keep = T, test.statistic = "F") %>%
modify_caption("Weighted linear regression results for fasting glucose (mmol/L)\n
(Females age 45y and older)")
fit.ex131 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex131, df.resid=degf(fit.ex131$survey.design))
confint(fit.ex131, ddf.resid=degf(fit.ex131$survey.design))
OR.CI <- cbind(exp( coef(fit.ex131)),
exp(confint(fit.ex131,
df.resid=degf(fit.ex131$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex132 <- svyglm(CVD ~ RIDRETH1, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex132, df.resid=degf(fit.ex132$survey.design))
confint(fit.ex132, ddf.resid=degf(fit.ex132$survey.design))
OR.CI <- cbind(exp( coef(fit.ex132)),
exp(confint(fit.ex132,
df.resid=degf(fit.ex132$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex133 <- svyglm(CVD ~ DMDEDUC2_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex133, df.resid=degf(fit.ex133$survey.design))
confint(fit.ex133, ddf.resid=degf(fit.ex133$survey.design))
OR.CI <- cbind(exp( coef(fit.ex133)),
exp(confint(fit.ex133,
df.resid=degf(fit.ex133$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex134 <- svyglm(CVD ~ DMDMARTL_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex134, df.resid=degf(fit.ex134$survey.design))
confint(fit.ex134, ddf.resid=degf(fit.ex341$survey.design))
OR.CI <- cbind(exp( coef(fit.ex134)),
exp(confint(fit.ex134,
df.resid=degf(fit.ex134$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex135 <- svyglm(CVD ~
HSD010_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex135, df.resid=degf(fit.ex135$survey.design))
confint(fit.ex135, ddf.resid=degf(fit.ex135$survey.design))
OR.CI <- cbind(exp( coef(fit.ex135)),
exp(confint(fit.ex135,
df.resid=degf(fit.ex134$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex136 <- svyglm(CVD ~ SLQ050_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex136, df.resid=degf(fit.ex136$survey.design))
confint(fit.ex136, ddf.resid=degf(fit.ex136$survey.design))
OR.CI <- cbind(exp( coef(fit.ex136)),
exp(confint(fit.ex136,
df.resid=degf(fit.ex136$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex137 <- svyglm(CVD ~ DIQ160_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex137, df.resid=degf(fit.ex137$survey.design))
confint(fit.ex137, ddf.resid=degf(fit.ex137$survey.design))
OR.CI <- cbind(exp( coef(fit.ex137)),
exp(confint(fit.ex137,
df.resid=degf(fit.ex137$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex138 <- svyglm(CVD ~ MCQ010_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex138, df.resid=degf(fit.ex138$survey.design))
confint(fit.ex138, ddf.resid=degf(fit.ex138$survey.design))
OR.CI <- cbind(exp( coef(fit.ex138)),
exp(confint(fit.ex138,
df.resid=degf(fit.ex138$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex139 <- svyglm(CVD ~ MCQ080_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex139, df.resid=degf(fit.ex139$survey.design))
confint(fit.ex139, ddf.resid=degf(fit.ex139$survey.design))
OR.CI <- cbind(exp( coef(fit.ex139)),
exp(confint(fit.ex139,
df.resid=degf(fit.ex139$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex141 <- svyglm(CVD ~ MCQ160F_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex141, df.resid=degf(fit.ex141$survey.design))
confint(fit.ex141, ddf.resid=degf(fit.ex141$survey.design))
OR.CI <- cbind(exp( coef(fit.ex141)),
exp(confint(fit.ex141,
df.resid=degf(fit.ex141$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex142 <- svyglm(CVD ~ MCQ220_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex142, df.resid=degf(fit.ex142$survey.design))
confint(fit.ex142, ddf.resid=degf(fit.ex142$survey.design))
OR.CI <- cbind(exp( coef(fit.ex142)),
exp(confint(fit.ex142,
df.resid=degf(fit.ex142$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex143 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char, family=quasibinomial(),
design = design.FST.domain131)
summary(fit.ex143, df.resid=degf(fit.ex143$survey.design))
confint(fit.ex143, ddf.resid=degf(fit.ex143$survey.design))
OR.CI <- cbind(exp( coef(fit.ex143)),
exp(confint(fit.ex143,
df.resid=degf(fit.ex143$survey.design))))[-1,]
round(OR.CI, 3)
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain131)
confint(svymean(~RIDAGEYR, design.FST.domain131), df = degf(design.FST.domain131))
#95% ci for prevalence
svyby(~CVD_char,~RIDRETH1, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD,~DMDEDUC2, design.FST.domain131, svymean)
confint(svyby(~CVD,~DMDEDUC2, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD,~DMDMARTL, design.FST.domain131, svymean)
confint(svyby(~CVD,~DMDMARTL, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD,~HSD010, design.FST.domain131, svymean)
confint(svyby(~CVD,~HSD010, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD_char,~SLQ050_char, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~SLQ050_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD_char,~DIQ160_char, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~DIQ160_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD_char,~MCQ010_char, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~MCQ010_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD_char,~MCQ080_char, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~MCQ080_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD_char,~MCQ160F_char, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~MCQ160F_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~MCQ220_char, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~MCQ220_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~DMDEDUC2, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~DMDEDUC2, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD_char,~DMDMARTL, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~DMDMARTL, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1_char,~HSD010, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~HSD010, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD_char,~SLQ050, design.FST.domain131, svymean)
confint(svyby(~CVD_char,~SLQ050, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
svyby(~CVD1,~DMDEDUC2_char, design.FST.domain131, svymean)
confint(svyby(~CVD,~DMDEDUC2_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
confint(svyby(~CVD1,~DMDMARTL_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
confint(svyby(~CVD1,~HSD010_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
confint(svyby(~CVD_char,~DMDMARTL_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
confint(svyby(~CVD_char,~DMDMARTL_char, design.FST.domain131, svymean),
df = degf(design.FST.domain131))
confint(svyby(~CVD_char, ~DMDMARTL_char, design.FST.domain131, svymean))
confint(svyby(~CVD_char,~DMDMARTL_char, design.FST131, svymean),
df = degf(design.FST131))
Hearing Problem NHANES
install.packages(c('tidyverse', "survey"))
library(tidyverse)
library(survey)
library(gtsummary)
nhanes.mod160 <- Merge_data_9_10_children_cleaned %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
RIDGEYR_char=case_when(RIDGEYR=="1"~ "1_5 years", RIDGEYR=="2"~"6_10 years " ,
RIDGEYR=="3"~ "11_15 years", RIDGEYR=="4"~ "16_19 years"),
RIAGENDR_char=case_when( RIAGENDR == "1" ~ "Male", RIAGENDR== "2" ~ "Female"),
AUQ010_char=case_when(AUQ010=="0"~"gOOd", AUQ010=="1"~"Problem"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(RIAGENDR) & !is.na(RIDRETH1) & !is.na(AUQ010) &
!is.na(RIDGEYR) &
WTMEC2YR > 0)
nhanes.mod161 <- nhanes.mod160 %>%
mutate(domain = nomiss)
design.FST161 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod161)
design.FST.domain161 <- subset(design.FST161, domain)
library(gtsummary)
design.FST.domain161 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = AUQ010_char,
# Use include to select variables
include = c(RIAGENDR_char,RIDGEYR_char, RIDRETH1_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by Races") %>%
bold_labels()
#95% ci for prevalence
svyby(~AUQ010_char,~RIDGEYR_char, design.FST.domain161, svymean)
confint(svyby(~AUQ010_char,~RIDGEYR_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~AUQ010_char,~RIDRETH1_char, design.FST.domain161, svymean)
confint(svyby(~AUQ010_char,~RIDRETH1_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
svyby(~AUQ010_char,~RIAGENDR_char, design.FST.domain161, svymean)
confint(svyby(~AUQ010_char,~RIAGENDR_char, design.FST.domain161, svymean),
df = degf(design.FST.domain161))
#####2011_2012
nhanes.mod260 <- Merge_data_11_12_children_cleaned %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
RIDGEYR_char=case_when(RIDGEYR=="1"~ "1_5 years", RIDGEYR=="2"~"6_10 years " ,
RIDGEYR=="3"~ "11_15 years", RIDGEYR=="4"~ "16_19 years"),
RIAGENDR_char=case_when( RIAGENDR == "1" ~ "Male", RIAGENDR== "2" ~ "Female"),
AUQ010_char=case_when(AUQ010=="0"~"gOOd", AUQ010=="1"~"Problem"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(RIAGENDR) & !is.na(RIDRETH1) & !is.na(AUQ010) &
!is.na(RIDGEYR) &
WTMEC2YR > 0)
nhanes.mod261 <- nhanes.mod260 %>%
mutate(domain = nomiss)
design.FST261 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod261)
design.FST.domain261 <- subset(design.FST261, domain)
library(gtsummary)
design.FST.domain261 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = AUQ010_char,
# Use include to select variables
include = c(RIAGENDR_char,RIDGEYR_char, RIDRETH1_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by Races") %>%
bold_labels()
#95% ci for prevalence
svyby(~AUQ010_char,~RIDGEYR_char, design.FST.domain261, svymean)
confint(svyby(~AUQ010_char,~RIDGEYR_char, design.FST.domain261, svymean),
df = degf(design.FST.domain261))
svyby(~AUQ010_char,~RIDRETH1_char, design.FST.domain261, svymean)
confint(svyby(~AUQ010_char,~RIDRETH1_char, design.FST.domain261, svymean),
df = degf(design.FST.domain261))
svyby(~AUQ010_char,~RIAGENDR_char, design.FST.domain261, svymean)
confint(svyby(~AUQ010_char,~RIAGENDR_char, design.FST.domain261, svymean),
df = degf(design.FST.domain261))
#####2015_2016
nhanes.mod360 <- Merge_data_15_16_children_cleaned %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
RIDGEYR_char=case_when(RIDGEYR=="1"~ "1_5 years", RIDGEYR=="2"~"6_10 years " ,
RIDGEYR=="3"~ "11_15 years", RIDGEYR=="4"~ "16_19 years"),
RIAGENDR_char=case_when( RIAGENDR == "1" ~ "Male", RIAGENDR== "2" ~ "Female"),
AUQ010_char=case_when(AUQ010=="0"~"gOOd", AUQ010=="1"~"Problem"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(RIAGENDR) & !is.na(RIDRETH1) & !is.na(AUQ010) &
!is.na(RIDGEYR) &
WTMEC2YR > 0)
nhanes.mod361 <- nhanes.mod360 %>%
mutate(domain = nomiss)
design.FST361 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod361)
design.FST.domain361 <- subset(design.FST361, domain)
library(gtsummary)
design.FST.domain361 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = AUQ010_char,
# Use include to select variables
include = c(RIAGENDR_char,RIDGEYR_char, RIDRETH1_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by Races") %>%
bold_labels()
#95% ci for prevalence
svyby(~AUQ010_char,~RIDGEYR_char, design.FST.domain361, svymean)
confint(svyby(~AUQ010_char,~RIDGEYR_char, design.FST.domain361, svymean),
df = degf(design.FST.domain361))
svyby(~AUQ010_char,~RIDRETH1_char, design.FST.domain361, svymean)
confint(svyby(~AUQ010_char,~RIDRETH1_char, design.FST.domain361, svymean),
df = degf(design.FST.domain361))
svyby(~AUQ010_char,~RIAGENDR_char, design.FST.domain361, svymean)
confint(svyby(~AUQ010_char,~RIAGENDR_char, design.FST.domain361, svymean),
df = degf(design.FST.domain361))
#####2017_2018
nhanes.mod460 <- Merge_data_17_18_children_cleaned %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
RIDGEYR_char=case_when(RIDGEYR=="1"~ "1_5 years", RIDGEYR=="2"~"6_10 years " ,
RIDGEYR=="3"~ "11_15 years", RIDGEYR=="4"~ "16_19 years"),
RIAGENDR_char=case_when( RIAGENDR == "1" ~ "Male", RIAGENDR== "2" ~ "Female"),
AUQ010_char=case_when(AUQ010=="0"~"gOOd", AUQ010=="1"~"Problem"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(RIAGENDR) & !is.na(RIDRETH1) & !is.na(AUQ010) &
!is.na(RIDGEYR) &
WTMEC2YR > 0)
nhanes.mod461 <- nhanes.mod460 %>%
mutate(domain = nomiss)
design.FST461 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod461)
design.FST.domain461 <- subset(design.FST461, domain)
library(gtsummary)
design.FST.domain461 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = AUQ010_char,
# Use include to select variables
include = c(RIAGENDR_char,RIDGEYR_char, RIDRETH1_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by Races") %>%
bold_labels()
#95% ci for prevalence
svyby(~AUQ010_char,~RIDGEYR_char, design.FST.domain461, svymean)
confint(svyby(~AUQ010_char,~RIDGEYR_char, design.FST.domain461, svymean),
df = degf(design.FST.domain461))
svyby(~AUQ010_char,~RIDRETH1_char, design.FST.domain461, svymean)
confint(svyby(~AUQ010_char,~RIDRETH1_char, design.FST.domain461, svymean),
df = degf(design.FST.domain461))
svyby(~AUQ010_char,~RIAGENDR_char, design.FST.domain461, svymean)
confint(svyby(~AUQ010_char,~RIAGENDR_char, design.FST.domain461, svymean),
df = degf(design.FST.domain461))
#####combined
nhanes.mod960 <- Combined_ %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
RIDGEYR_char=case_when(RIDGEYR=="1"~ "1_5 years", RIDGEYR=="2"~"6_10 years " ,
RIDGEYR=="3"~ "11_15 years", RIDGEYR=="4"~ "16_19 years"),
RIAGENDR_char=case_when( RIAGENDR == "1" ~ "Male", RIAGENDR== "2" ~ "Female"),
AUQ010_char=case_when(AUQ010=="0"~"gOOd", AUQ010=="1"~"Problem"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(RIAGENDR) & !is.na(RIDRETH1) & !is.na(AUQ010) &
!is.na(RIDGEYR) &
WTMEC2YR > 0)
nhanes.mod961 <- nhanes.mod960 %>%
mutate(domain = nomiss)
design.FST961 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod961)
design.FST.domain961 <- subset(design.FST961, domain)
library(gtsummary)
design.FST.domain961 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = AUQ010_char,
# Use include to select variables
include = c(RIAGENDR_char,RIDGEYR_char, RIDRETH1_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by Races") %>%
bold_labels()
fit.ex64 <- svyglm(AUQ010 ~ RIAGENDR_char, family=quasibinomial(),
design = design.FST.domain961)
summary(fit.ex64, df.resid=degf(fit.ex64$survey.design))
confint(fit.ex64, ddf.resid=degf(fit.ex64$survey.design))
OR.CI <- cbind(exp( coef(fit.ex64)),
exp(confint(fit.ex64,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex65 <- svyglm(AUQ010 ~ RIDGEYR_char, family=quasibinomial(),
design = design.FST.domain961)
summary(fit.ex65, df.resid=degf(fit.ex65$survey.design))
confint(fit.ex65, ddf.resid=degf(fit.ex65$survey.design))
OR.CI <- cbind(exp( coef(fit.ex65)),
exp(confint(fit.ex65,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex66 <- svyglm(AUQ010 ~ RIDRETH1_char, family=quasibinomial(),
design = design.FST.domain961)
summary(fit.ex66, df.resid=degf(fit.ex66$survey.design))
confint(fit.ex66, ddf.resid=degf(fit.ex66$survey.design))
OR.CI <- cbind(exp( coef(fit.ex66)),
exp(confint(fit.ex66,
df.resid=degf(fit.ex66$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex67 <- svyglm(AUQ010 ~ RIDRETH1_char+RIDGEYR_char+RIAGENDR_char, family=quasibinomial(),
design = design.FST.domain961)
summary(fit.ex67, df.resid=degf(fit.ex66$survey.design))
confint(fit.ex67, ddf.resid=degf(fit.ex66$survey.design))
OR.CI <- cbind(exp( coef(fit.ex67)),
exp(confint(fit.ex67,
df.resid=degf(fit.ex66$survey.design))))[-1,]
round(OR.CI, 3)
#vizulization practice
install.packages("ggplot2")
library(ggplot2)
# turn-off scientific notation like 1e+48
options(scipen=999)
theme_set(theme_bw())
data('design.FST.domain671' , package = "ggplot2")
k1<-ggplot(VIZUALIZATION, aes(x = Year, y = Age_16_19_years, group = 1)) +
geom_line() +
geom_point()
plot<- ggarrange(k1, ncol = 1, nrow = 1,
common.legend = TRUE,legend="bottom")
annotate_figure(plot, top = text_grob("Weighted Trend prevalence(%) of hearing problem among US children ",
color = "red", face = "bold", size = 14))
k2<-ggplot(practice_vizualization, aes(x = Year, y = Asthma, group = 1)) +
geom_line() +
geom_point()
k3<-ggplot(practice_vizualization, aes(x = Year, y =Stroke, group = 1)) +
geom_line() +
geom_point()
k4<-ggplot(practice_vizualization, aes(x = Year, y = Cancer, group = 1)) +
geom_line() +
geom_point()
k5<-ggplot(practice_vizualization, aes(x = Year, y = Edu_less_9th_grade, group = 1)) +
geom_line() +
geom_point()
k6<-ggplot(practice_vizualization, aes(x = Year, y = Good_Health, group = 1)) +
geom_line() +
geom_point()
k7<-ggplot(practice_vizualization, aes(x = Year, y = Fair_Health, group = 1)) +
geom_line() +
geom_point()
install.packages(' gridExtra')
library( gridExtra)
ggarrange(k1, k2,k3,k4,k5,k6,k7, ncol = 2, nrow = 4)
install.packages("ggpubr")
library(ggpubr)
plot<- ggarrange(k1, k2,k3,k4,k5,k6,k7, ncol = 2, nrow = 4,
common.legend = TRUE,legend="bottom")
annotate_figure(plot, top = text_grob("Weighted Significant Trend prevalence(%) of CHD for Black African American women with hysterectomy those not having regular periods ",
color = "red", face = "bold", size = 14))
Electronic Cigarrete
install.packages(c('tidyverse', "survey"))
library(tidyverse)
library(survey)
library(gtsummary)
############ 2015-2016
nrow(E_cigarette2015_2016)
nhanes.mod120 <- E_cigarette2015_2016 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other",
RIDRETH1=="3"~"white",
RIDRETH1=="4"~"black",
RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ",
DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent",
DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married" ,
DMDMARTL=="2"~"Widowed" ,
DMDMARTL=="3"~ " Divorced",
DMDMARTL=="4"~"Separated" ,
DMDMARTL=="5"~"Never married" ,
DMDMARTL=="6"~ "Living with partner"),
SMQ900_char=case_when(SMQ900=="1"~"yes",
SMQ900=="2"~"no"),
RIAGENDR_char=case_when(RIAGENDR == "1" ~ "male",
RIAGENDR == "2" ~ "female") ,
MCQ160M_char = case_when(MCQ160M == "1" ~ "yes",
MCQ160M == "2" ~ "No"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes",
MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes",
MCQ080 == "2" ~ "No"),
MCQ160A_char=case_when(MCQ160A == "1" ~ "yes",
MCQ160A== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes",
MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO",
CVD== "1" ~ "YES"),
MCQ160G_char = case_when(MCQ160G == "1" ~ "yes",
MCQ160G == "2" ~ "No"),
MCQ160K_char=case_when(MCQ160K == "1" ~ "yes",
MCQ160K == "2" ~ "No") ,
MCQ160O_char=case_when(MCQ160O== "1" ~ "Yes",
MCQ160O == "2" ~ "No"),
MCQ203_char=case_when(MCQ203 == "1" ~ "yes",
MCQ203== "2" ~ "No"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(MCQ220) &!is.na(DMDMARTL) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(CVD) &
!is.na(DMDEDUC2) & !is.na(SMQ900) & !is.na(MCQ203) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160A) & !is.na(MCQ220) & !is.na(MCQ160M)&
!is.na(MCQ160G) & !is.na( MCQ160K) & !is.na(MCQ160O) & WTMEC2YR > 0)
nhanes.mod121 <- nhanes.mod120 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST121 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod121)
design.FST.domain121 <- subset(design.FST121, domain)
library(gtsummary)
design.FST.domain121 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = SMQ900_char,
# Use include to select variables
include = c(RIAGENDR_char, RIDAGEYR, RIDRETH1_char, DMDEDUC2_char,
DMDMARTL_char, MCQ010_char, MCQ080_char,
MCQ160A_char, MCQ160M_char, MCQ160G_char, MCQ160K_char,
MCQ160O_char, MCQ203_char, MCQ220_char,CVD_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, E_cigarette") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain121)
confint(svymean(~RIDAGEYR, design.FST.domain121), df = degf(design.FST.domain121))
#final prevalence and CI
#95% ci for prevalence
svyby(~SMQ900_char,~RIAGENDR_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~RIAGENDR_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~RIDRETH1_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~RIDRETH1_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~DMDEDUC2_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~DMDEDUC2_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~DMDMARTL_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~DMDMARTL_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~MCQ010_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~MCQ010_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~MCQ080_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~MCQ080_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~ MCQ160A_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~ MCQ160A_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~MCQ160M_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~MCQ160M_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~MCQ160G_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~MCQ160G_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~MCQ160K_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~MCQ160K_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~MCQ160O_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~MCQ160O_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~MCQ203_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~MCQ203_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~MCQ220_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~MCQ220_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~SMQ900_char,~CVD_char, design.FST.domain121, svymean)
confint(svyby(~SMQ900_char,~CVD_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
############ 2017-2018
nrow(E_cigarette2017_2018)
nhanes.mod220 <- E_cigarette2017_2018 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other",
RIDRETH1=="3"~"white",
RIDRETH1=="4"~"black",
RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ",
DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent",
DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married" ,
DMDMARTL=="2"~"Widowed" ,
DMDMARTL=="3"~ " Divorced",
DMDMARTL=="4"~"Separated" ,
DMDMARTL=="5"~"Never married" ,
DMDMARTL=="6"~ "Living with partner"),
SMQ900_char=case_when(SMQ900=="1"~"yes",
SMQ900=="2"~"no"),
RIAGENDR_char=case_when(RIAGENDR == "1" ~ "male",
RIAGENDR == "2" ~ "female") ,
MCQ160M_char = case_when(MCQ160M == "1" ~ "yes",
MCQ160M == "2" ~ "No"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes",
MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes",
MCQ080 == "2" ~ "No"),
MCQ160A_char=case_when(MCQ160A == "1" ~ "yes",
MCQ160A== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes",
MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO",
CVD== "1" ~ "YES"),
MCQ160G_char = case_when(MCQ160G == "1" ~ "yes",
MCQ160G == "2" ~ "No"),
MCQ160K_char=case_when(MCQ160K == "1" ~ "yes",
MCQ160K == "2" ~ "No") ,
MCQ160O_char=case_when(MCQ160O== "1" ~ "Yes",
MCQ160O == "2" ~ "No"),
MCQ203_char=case_when(MCQ203 == "1" ~ "yes",
MCQ203== "2" ~ "No"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(MCQ220) &!is.na(DMDMARTL) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(CVD) &
!is.na(DMDEDUC2) & !is.na(SMQ900) & !is.na(MCQ203) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160A) & !is.na(MCQ220) & !is.na(MCQ160M)&
!is.na(MCQ160G) & !is.na( MCQ160K) & !is.na(MCQ160O) & WTMEC2YR > 0)
nhanes.mod221 <- nhanes.mod220 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST221 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod221)
design.FST.domain221 <- subset(design.FST221, domain)
library(gtsummary)
design.FST.domain221 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = SMQ900_char,
# Use include to select variables
include = c(RIAGENDR_char, RIDAGEYR, RIDRETH1_char, DMDEDUC2_char,
DMDMARTL_char, MCQ010_char, MCQ080_char,
MCQ160A_char, MCQ160M_char, MCQ160G_char, MCQ160K_char,
MCQ160O_char, MCQ203_char, MCQ220_char,CVD_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, E_cigarette") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain221)
confint(svymean(~RIDAGEYR, design.FST.domain221),
df = degf(design.FST.domain221))
#final prevalence and CI
#95% ci for prevalence
svyby(~SMQ900_char,~RIAGENDR_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~RIAGENDR_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~RIDRETH1_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~RIDRETH1_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~DMDEDUC2_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~DMDEDUC2_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~DMDMARTL_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~DMDMARTL_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~MCQ010_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~MCQ010_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~MCQ080_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~MCQ080_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~ MCQ160A_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~ MCQ160A_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~MCQ160M_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~MCQ160M_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~MCQ160G_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~MCQ160G_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~MCQ160K_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~MCQ160K_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~MCQ160O_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~MCQ160O_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~MCQ203_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~MCQ203_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~MCQ220_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~MCQ220_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~SMQ900_char,~CVD_char, design.FST.domain221, svymean)
confint(svyby(~SMQ900_char,~CVD_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
############combined
nhanes.mod670 <- Combined_all_ %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other",
RIDRETH1=="3"~"white",
RIDRETH1=="4"~"black",
RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ",
DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent",
DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married" ,
DMDMARTL=="2"~"Widowed" ,
DMDMARTL=="3"~ " Divorced",
DMDMARTL=="4"~"Separated" ,
DMDMARTL=="5"~"Never married" ,
DMDMARTL=="6"~ "Living with partner"),
SMQ900_char=case_when(SMQ900=="0"~"no",
SMQ900=="1"~"yes"),
RIAGENDR_char=case_when(RIAGENDR == "1" ~ "male",
RIAGENDR == "2" ~ "female") ,
MCQ160M_char = case_when(MCQ160M == "1" ~ "yes",
MCQ160M == "2" ~ "No"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes",
MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes",
MCQ080 == "2" ~ "No"),
MCQ160A_char=case_when(MCQ160A == "1" ~ "yes",
MCQ160A== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes",
MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO",
CVD== "1" ~ "YES"),
MCQ160G_char = case_when(MCQ160G == "1" ~ "yes",
MCQ160G == "2" ~ "No"),
MCQ160K_char=case_when(MCQ160K == "1" ~ "yes",
MCQ160K == "2" ~ "No") ,
MCQ160O_char=case_when(MCQ160O== "1" ~ "Yes",
MCQ160O == "2" ~ "No"),
MCQ203_char=case_when(MCQ203 == "1" ~ "yes",
MCQ203== "2" ~ "No"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(MCQ220) &!is.na(DMDMARTL) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(CVD) &
!is.na(DMDEDUC2) & !is.na(SMQ900) & !is.na(MCQ203) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160A) & !is.na(MCQ220) & !is.na(MCQ160M)&
!is.na(MCQ160G) & !is.na( MCQ160K) & !is.na(MCQ160O) & WTMEC2YR > 0)
nhanes.mod671 <- nhanes.mod670 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST671 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod671)
design.FST.domain671 <- subset(design.FST671, domain)
library(gtsummary)
design.FST.domain671 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = SMQ900_char,
# Use include to select variables
include = c(RIAGENDR_char, RIDAGEYR, RIDRETH1_char, DMDEDUC2_char,
DMDMARTL_char, MCQ010_char, MCQ080_char,
MCQ160A_char, MCQ160M_char, MCQ160G_char, MCQ160K_char,
MCQ160O_char, MCQ203_char, MCQ220_char,CVD_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Sample Size and Weighted Sample
Characteristics by CVD Status Among Black African American with hysterectomy,
1999-2016") %>%
bold_labels()
fit.ex671 <- svyglm(SMQ900~ RIAGENDR_char+RIDAGEYR+ RIDRETH1_char+ DMDEDUC2_char+
DMDMARTL_char+ MCQ010_char+ MCQ080_char+
MCQ160A_char+ MCQ160M_char+ MCQ160G_char+MCQ160K_char+
MCQ160O_char+ MCQ203_char+ MCQ220_char+CVD_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex671, df.resid=degf(fit.ex671$survey.design))
confint(fit.ex671, ddf.resid=degf(fit.ex671$survey.design))
OR.CI <- cbind(exp( coef(fit.ex671)),
exp(confint(fit.ex671,
df.resid=degf(fit.ex671$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex672 <- svyglm(SMQ900 ~ RIAGENDR_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex672, df.resid=degf(fit.ex672$survey.design))
confint(fit.ex672, ddf.resid=degf(fit.ex672$survey.design))
OR.CI <- cbind(exp( coef(fit.ex672)),
exp(confint(fit.ex672,
df.resid=degf(fit.ex672$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex63 <- svyglm(SMQ900 ~ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex63, df.resid=degf(fit.ex63$survey.design))
confint(fit.ex63, ddf.resid=degf(fit.ex63$survey.design))
OR.CI <- cbind(exp( coef(fit.ex63)),
exp(confint(fit.ex63,
df.resid=degf(fit.ex63$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex64 <- svyglm(SMQ900 ~ RIDRETH1_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex64, df.resid=degf(fit.ex64$survey.design))
confint(fit.ex64, ddf.resid=degf(fit.ex64$survey.design))
OR.CI <- cbind(exp( coef(fit.ex64)),
exp(confint(fit.ex64,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex65 <- svyglm(SMQ900 ~ DMDEDUC2_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex65, df.resid=degf(fit.ex65$survey.design))
confint(fit.ex65, ddf.resid=degf(fit.ex65$survey.design))
OR.CI <- cbind(exp( coef(fit.ex65)),
exp(confint(fit.ex65,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex66 <- svyglm(SMQ900 ~ DMDMARTL_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex66, df.resid=degf(fit.ex66$survey.design))
confint(fit.ex66, ddf.resid=degf(fit.ex66$survey.design))
OR.CI <- cbind(exp( coef(fit.ex66)),
exp(confint(fit.ex66,
df.resid=degf(fit.ex66$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex67 <- svyglm(SMQ900 ~ MCQ010_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex67, df.resid=degf(fit.ex67$survey.design))
confint(fit.ex67, ddf.resid=degf(fit.ex67$survey.design))
OR.CI <- cbind(exp( coef(fit.ex67)),
exp(confint(fit.ex67,
df.resid=degf(fit.ex67$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex68 <- svyglm(SMQ900 ~ MCQ080_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex68, df.resid=degf(fit.ex68$survey.design))
confint(fit.ex68, ddf.resid=degf(fit.ex68$survey.design))
OR.CI <- cbind(exp( coef(fit.ex68)),
exp(confint(fit.ex68,
df.resid=degf(fit.ex68$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex69 <- svyglm(SMQ900 ~ MCQ160A_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex69, df.resid=degf(fit.ex69$survey.design))
confint(fit.ex69, ddf.resid=degf(fit.ex69$survey.design))
OR.CI <- cbind(exp( coef(fit.ex69)),
exp(confint(fit.ex69,
df.resid=degf(fit.ex69$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex72 <- svyglm(SMQ900 ~ MCQ160M_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex72, df.resid=degf(fit.ex72$survey.design))
confint(fit.ex72, ddf.resid=degf(fit.ex72$survey.design))
OR.CI <- cbind(exp( coef(fit.ex72)),
exp(confint(fit.ex72,
df.resid=degf(fit.ex72$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex73 <- svyglm(SMQ900 ~ MCQ160G_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex73, df.resid=degf(fit.ex73$survey.design))
confint(fit.ex73, ddf.resid=degf(fit.ex73$survey.design))
OR.CI <- cbind(exp( coef(fit.ex73)),
exp(confint(fit.ex73,
df.resid=degf(fit.ex73$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex73 <- svyglm(SMQ900 ~ MCQ160K_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex73, df.resid=degf(fit.ex73$survey.design))
confint(fit.ex73, ddf.resid=degf(fit.ex73$survey.design))
OR.CI <- cbind(exp( coef(fit.ex73)),
exp(confint(fit.ex73,
df.resid=degf(fit.ex73$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex66 <- svyglm(SMQ900 ~ MCQ160O_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex66, df.resid=degf(fit.ex66$survey.design))
confint(fit.ex66, ddf.resid=degf(fit.ex66$survey.design))
OR.CI <- cbind(exp( coef(fit.ex66)),
exp(confint(fit.ex66,
df.resid=degf(fit.ex66$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex67 <- svyglm(SMQ900 ~ MCQ203_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex67, df.resid=degf(fit.ex67$survey.design))
confint(fit.ex67, ddf.resid=degf(fit.ex67$survey.design))
OR.CI <- cbind(exp( coef(fit.ex67)),
exp(confint(fit.ex67,
df.resid=degf(fit.ex67$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex68 <- svyglm(SMQ900 ~ MCQ220_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex68, df.resid=degf(fit.ex68$survey.design))
confint(fit.ex68, ddf.resid=degf(fit.ex68$survey.design))
OR.CI <- cbind(exp( coef(fit.ex68)),
exp(confint(fit.ex68,
df.resid=degf(fit.ex68$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex69 <- svyglm(SMQ900 ~ CVD_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex69, df.resid=degf(fit.ex69$survey.design))
confint(fit.ex69, ddf.resid=degf(fit.ex69$survey.design))
OR.CI <- cbind(exp( coef(fit.ex69)),
exp(confint(fit.ex69,
df.resid=degf(fit.ex69$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex71 <- svyglm(CVD ~ MCQ160F_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex71, df.resid=degf(fit.ex71$survey.design))
confint(fit.ex71, ddf.resid=degf(fit.ex71$survey.design))
OR.CI <- cbind(exp( coef(fit.ex71)),
exp(confint(fit.ex71,
df.resid=degf(fit.ex71$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex72 <- svyglm(CVD ~ SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex72, df.resid=degf(fit.ex72$survey.design))
confint(fit.ex72, ddf.resid=degf(fit.ex72$survey.design))
OR.CI <- cbind(exp( coef(fit.ex72)),
exp(confint(fit.ex72,
df.resid=degf(fit.ex72$survey.design))))[-1,]
round(OR.CI, 3)
#####vizualiaztions
install.packages("ggplot2")
library(ggplot2)
# turn-off scientific notation like 1e+48
options(scipen=999)
theme_set(theme_bw())
data('design.FST.domain671' , package = "ggplot2")
k1<-ggplot(Vizualization, aes(x = Year, y = Gender_male, group = 1)) +
geom_line() +
geom_point()
k2<-ggplot(Vizualization, aes(x = Year, y = Cancer, group = 1)) +
geom_line() +
geom_point()
k3<-ggplot(Vizualization, aes(x = Year, y =Chronic_bronchitis, group = 1)) +
geom_line() +
geom_point()
k4<-ggplot(Vizualization, aes(x = Year, y = Arthritis, group = 1)) +
geom_line() +
geom_point()
k5<-ggplot(Vizualization, aes(x = Year, y = Never_married, group = 1)) +
geom_line() +
geom_point()
k6<-ggplot(Vizualization, aes(x = Year, y = Living_with_partner , group = 1)) +
geom_line() +
geom_point()
k7<-ggplot(Vizualization, aes(x = Year, y = Edu_Less_than_9th_grade, group = 1)) +
geom_line() +
geom_point()
k8<-ggplot(Vizualization, aes(x = Year, y = College_graduate_or_above, group = 1)) +
geom_line() +
geom_point()
install.packages(' gridExtra')
library( gridExtra)
ggarrange(k1, k2,k3,k4,k5,k6,k7, ncol = 2, nrow = 4)
install.packages("ggpubr")
library(ggpubr)
plot<- ggarrange(k3, k2,k6,k4,k7,k5,k1,k8, ncol = 2, nrow = 4,
common.legend = TRUE,legend="bottom")
annotate_figure(plot, top = text_grob("Weighted Significant Trend prevalence(%) of E-Cigarette for Black African American ",
color = "red", face = "bold", size = 14))
#######Drug
nhanes.mod981 <- merged17_18_practices %>%
mutate( RIDRETH1 == "4")
design.FST981 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod981)
svyby(~RIAGENDR,~RXDDRUG, design.FST981, svymean)
confint(svyby(~RIAGENDR,~RXDDRUG, design.FST981, svymean),
df = degf(design.FST981))
svyby(~RIAGENDR,~RXDRSD1, design.FST981, svymean)
confint(svyby(~RIAGENDR,~RXDRSD1, design.FST981, svymean),
df = degf(design.FST981))
Drug
install.packages(c('tidyverse', "survey"))
library(tidyverse)
library(survey)
library(gtsummary)
#### combined
library(gtsummary)
nhanes.mod670 <- combined1 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(Races=="1"~"Maxican",
Races=="2"~"other", Races=="3"~"white",
Races=="4"~"black", Races=="5"~"multi"),
Education_char=case_when(Education=="1"~ "Less than 9th grade ", Education=="2"~"9-11th grade " ,
Education=="3"~ "High school graduate/GED or equivalent",
Education=="4"~ "Some college or AA degree",
Education=="5"~"College graduate or above"),
Marital_StatusL_char=case_when(Marital_Status=="1"~"Married" ,
Marital_Status=="2"~"Widowed" ,
Marital_Status=="3"~ "Divorced",
Marital_Status=="1"~"Separated" ,
Marital_Status=="2"~"Never married" ,
Marital_Status=="3"~ "Living with Partner"),
Gender_char=case_when(Gender=="0"~"male",Gender=="1"~"female"),
overweight_char=case_when(overweight == "1" ~ "yes", overweight == "2" ~ "No") ,
Arthritis_char = case_when(Arthritis == "1" ~ "yes", Arthritis == "2" ~ "No"),
Thyriod_problem_char=case_when(Thyriod_problem == "1" ~ "yes", Thyriod_problem == "2" ~ "No") ,
Liver_char=case_when(Liver== "1" ~ "Yes", Liver == "2" ~ "No"),
Respiratory_char=case_when(Respiratory == "0" ~ "no", Respiratory== "1" ~ "yes"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(Gender) &!is.na(Races) & !is.na(Age) &
!is.na(Education) & !is.na(Marital_Status) & !is.na(overweight) & !is.na(Arthritis) &
!is.na(Thyriod_problem)&
!is.na(Liver) & !is.na(Cancer) & !is.na(Respiratory) & !is.na(CVD)& WTMEC2YR > 0)
nhanes.mod671 <- nhanes.mod670 %>%
mutate(domain = nomiss & Races == "4")
design.FST671 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod671)
design.FST.domain671 <- subset(design.FST671, domain)
library(gtsummary)
design.FST.domain671 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = Gender_char,
# Use include to select variables
include = c( Races_char, Age, Education_char, Marital_Status_char,
overweight_char, Arthritis_char, Thyriod_problem_char,
Liver_char, Cancer_char, Respiratory_char, CVD_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Sample Size and Weighted Sample
Characteristics by CVD Status Among Black African American with hysterectomy,
1999-2016") %>%
bold_labels()
design.FST.domain671 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = Gender_char,
# Use include to select variables
include = c( Races, Age, Education, Marital_Status,
overweight, Arthritis, Thyriod_problem,
Liver, Cancer, Respiratory, CVD),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Sample Size and Weighted Sample
Characteristics by CVD Status Among Black African American with hysterectomy,
1999-2016") %>%
bold_labels()
fit.ex672 <- svyglm(CVD ~ Gender_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex672, df.resid=degf(fit.ex672$survey.design))
confint(fit.ex672, ddf.resid=degf(fit.ex672$survey.design))
OR.CI <- cbind(exp( coef(fit.ex672)),
exp(confint(fit.ex672,
df.resid=degf(fit.ex672$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex63 <- svyglm(Respiratory ~ CVD_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex63, df.resid=degf(fit.ex63$survey.design))
confint(fit.ex63, ddf.resid=degf(fit.ex63$survey.design))
OR.CI <- cbind(exp( coef(fit.ex63)),
exp(confint(fit.ex63,
df.resid=degf(fit.ex63$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex64 <- svyglm(Respiratory~ Cancer, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex64, df.resid=degf(fit.ex64$survey.design))
confint(fit.ex64, ddf.resid=degf(fit.ex64$survey.design))
OR.CI <- cbind(exp( coef(fit.ex64)),
exp(confint(fit.ex64,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex65 <- svyglm(Respiratory ~ Thyriod_problem_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex65, df.resid=degf(fit.ex65$survey.design))
confint(fit.ex65, ddf.resid=degf(fit.ex65$survey.design))
OR.CI <- cbind(exp( coef(fit.ex65)),
exp(confint(fit.ex65,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex66 <- svyglm(Respiratory~ Liver_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex66, df.resid=degf(fit.ex66$survey.design))
confint(fit.ex66, ddf.resid=degf(fit.ex66$survey.design))
OR.CI <- cbind(exp( coef(fit.ex66)),
exp(confint(fit.ex66,
df.resid=degf(fit.ex66$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex67 <- svyglm(Respiratory ~ Arthritis_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex67, df.resid=degf(fit.ex67$survey.design))
confint(fit.ex67, ddf.resid=degf(fit.ex67$survey.design))
OR.CI <- cbind(exp( coef(fit.ex67)),
exp(confint(fit.ex67,
df.resid=degf(fit.ex67$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex68 <- svyglm(Gender ~ overweight_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex68, df.resid=degf(fit.ex68$survey.design))
confint(fit.ex68, ddf.resid=degf(fit.ex68$survey.design))
OR.CI <- cbind(exp( coef(fit.ex68)),
exp(confint(fit.ex68,
df.resid=degf(fit.ex68$survey.design))))[-1,]
round(OR.CI, 3)
Hysterectomy New
install.packages(c('tidyverse', "survey"))
library(tidyverse)
library(survey)
library(gtsummary)
######## Hysterectomy new 2011-2012
nrow(X2011_12)
nhanes.mod120 <- X2011_12 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when( RIDRETH1=="1"~"Maxican",RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDBORN4_char=case_when(DMDBORN4=="1"~"USA",DMDBORN4=="2"~"no"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " , DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree", DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married/Living with Partner" , DMDMARTL=="2"~"Widowed/Divorced/Separated" , DMDMARTL=="3"~ "Never married"),
HSD010_char=case_when(HSD010=="1"~"A",HSD010=="2"~"B",HSD010=="3"~"C", HSD010=="4"~"D",HSD010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
PAQ605_char=case_when(PAQ605=="1"~"yes", PAQ605=="2"~"no"),
PAQ620_char=case_when(PAQ620=="1"~"yes", PAQ620=="2"~"no"),
RHD280_char=case_when(RHD280=="1"~"yes", RHD280=="2"~"no"),
DIQ010_char = case_when(DIQ010 == "1" ~ "yes", DIQ010 == "2" ~ "No"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No"),
MCQ160A_char=case_when(MCQ160A == "1" ~ "yes",MCQ160A== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"),
Depression_char=case_when(Depression == "0" ~ "NO", Depression== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(HSD010) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(DMDMARTL) &
!is.na(DMDEDUC2) & !is.na(SLQ050) & !is.na(DIQ010) &
!is.na(RHD280) & !is.na(PAQ620) & !is.na(PAQ605) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160A) & !is.na(MCQ220)& !is.na(CVD)&
!is.na(Depression) & WTMEC2YR > 0)
nhanes.mod121 <- nhanes.mod120 %>%
mutate(domain = nomiss & RIDRETH1=="4")
design.FST121 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod121)
design.FST.domain121 <- subset(design.FST121, domain)
library(gtsummary)
design.FST.domain121 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(RIDAGEYR, INDFMPIR, DMDBORN4_char, DMDEDUC2_char, DMDMARTL_char,
HSD010_char, PAQ605_char, PAQ620_char ,RHD280_char, DIQ010_char , MCQ010_char,
MCQ080_char, MCQ160A_char, MCQ220_char, RIDRETH1, Depression_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by CVD") %>%
bold_labels()
#Frequency Recheck
prop.table(svytable(~CVD_char + SLQ050_char, design.FST.domain121), margin = 2)
design.FST.domain121 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(MCQ160F_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status\n
(Females age 45y and older)") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain121)
confint(svymean(~RIDAGEYR, design.FST.domain121), df = degf(design.FST.domain121))
svymean( ~INDFMPIR, design.FST.domain121)
confint(svymean(~INDFMPIR, design.FST.domain121), df = degf(design.FST.domain121))
#final prevalence and CI
#95% ci for prevalence
svyby(~RIDRETH1_char,~CVD_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~DMDBORN4_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~DMDBORN4_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~DMDEDUC2, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~DMDEDUC2, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char, ~DMDMARTL_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~DMDMARTL_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD,~HSD010_char, design.FST.domain121, svymean)
confint(svyby(~CVD,~HSD010_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD,~PAQ605_char, design.FST.domain121, svymean)
confint(svyby(~CVD,~PAQ605_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~PAQ620_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~PAQ620_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~RHD280_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~RHD280_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~DIQ010_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~DIQ010_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~MCQ010_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~MCQ010_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~MCQ080_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~MCQ080_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~MCQ160A_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~MCQ160A_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~MCQ220_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~MCQ220_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~CVD_char,~Depression_char, design.FST.domain121, svymean)
confint(svyby(~CVD_char,~Depression_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
######## Hysterectomy new 2013-2014
nrow(X2013_14)
nhanes.mod320 <- X2013_14 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when( RIDRETH1=="1"~"Maxican",RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDBORN4_char=case_when(DMDBORN4=="1"~"USA",DMDBORN4=="2"~"no"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " , DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree", DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married/Living with Partner" , DMDMARTL=="2"~"Widowed/Divorced/Separated" , DMDMARTL=="3"~ "Never married"),
HSD010_char=case_when(HSD010=="1"~"A",HSD010=="2"~"B",HSD010=="3"~"C", HSD010=="4"~"D",HSD010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
PAQ605_char=case_when(PAQ605=="1"~"yes", PAQ605=="2"~"no"),
PAQ620_char=case_when(PAQ620=="1"~"yes", PAQ620=="2"~"no"),
RHD280_char=case_when(RHD280=="1"~"yes", RHD280=="2"~"no"),
DIQ010_char = case_when(DIQ010 == "1" ~ "yes", DIQ010 == "2" ~ "No"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No"),
MCQ160A_char=case_when(MCQ160A == "1" ~ "yes",MCQ160A== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"),
Depression_char=case_when(Depression == "0" ~ "NO", Depression== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(HSD010) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(DMDMARTL) &
!is.na(DMDEDUC2) & !is.na(SLQ050) & !is.na(DIQ010) &
!is.na(RHD280) & !is.na(PAQ620) & !is.na(PAQ605) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160A) & !is.na(MCQ220)& !is.na(CVD)&
!is.na(Depression) & WTMEC2YR > 0)
nhanes.mod321 <- nhanes.mod320 %>%
mutate(domain = nomiss & RIDRETH1=="4")
design.FST321 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod321)
design.FST.domain321 <- subset(design.FST321, domain)
library(gtsummary)
design.FST.domain321 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(RIDAGEYR, INDFMPIR, DMDBORN4_char, DMDEDUC2_char, DMDMARTL_char,
HSD010_char, PAQ605_char, PAQ620_char ,RHD280_char, DIQ010_char , MCQ010_char,
MCQ080_char, MCQ160A_char, MCQ220_char, RIDRETH1, Depression_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by CVD") %>%
bold_labels()
#Frequency Recheck
prop.table(svytable(~CVD_char + SLQ050_char, design.FST.domain121), margin = 2)
design.FST.domain121 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(MCQ160F_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status\n
(Females age 45y and older)") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain321)
confint(svymean(~RIDAGEYR, design.FST.domain321), df = degf(design.FST.domain321))
svymean( ~INDFMPIR, design.FST.domain321)
confint(svymean(~INDFMPIR, design.FST.domain321), df = degf(design.FST.domain321))
#final prevalence and CI
#95% ci for prevalence
svyby(~RIDRETH1_char,~CVD_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char,~DMDBORN4_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~DMDBORN4_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char,~DMDEDUC2, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~DMDEDUC2, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char, ~DMDMARTL_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~DMDMARTL_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD,~HSD010_char, design.FST.domain321, svymean)
confint(svyby(~CVD,~HSD010_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD,~PAQ605_char, design.FST.domain321, svymean)
confint(svyby(~CVD,~PAQ605_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char,~PAQ620_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~PAQ620_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char,~RHD280_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~RHD280_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char,~DIQ010_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~DIQ010_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char,~MCQ010_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~MCQ010_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char,~MCQ080_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~MCQ080_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char,~MCQ160A_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~MCQ160A_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char,~MCQ220_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~MCQ220_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~CVD_char,~Depression_char, design.FST.domain321, svymean)
confint(svyby(~CVD_char,~Depression_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
######## Hysterectomy new 2015-2016
nhanes.mod420 <- X2015_16 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when( RIDRETH1=="1"~"Maxican",RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDBORN4_char=case_when(DMDBORN4=="1"~"USA",DMDBORN4=="2"~"no"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " , DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree", DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married/Living with Partner" , DMDMARTL=="2"~"Widowed/Divorced/Separated" , DMDMARTL=="3"~ "Never married"),
HSD010_char=case_when(HSD010=="1"~"A",HSD010=="2"~"B",HSD010=="3"~"C", HSD010=="4"~"D",HSD010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
PAQ605_char=case_when(PAQ605=="1"~"yes", PAQ605=="2"~"no"),
PAQ620_char=case_when(PAQ620=="1"~"yes", PAQ620=="2"~"no"),
RHD280_char=case_when(RHD280=="1"~"yes", RHD280=="2"~"no"),
DIQ010_char = case_when(DIQ010 == "1" ~ "yes", DIQ010 == "2" ~ "No"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No"),
MCQ160A_char=case_when(MCQ160A == "1" ~ "yes",MCQ160A== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"),
Depression_char=case_when(Depression == "0" ~ "NO", Depression== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(HSD010) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(DMDMARTL) &
!is.na(DMDEDUC2) & !is.na(SLQ050) & !is.na(DIQ010) &
!is.na(RHD280) & !is.na(PAQ620) & !is.na(PAQ605) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160A) & !is.na(MCQ220)& !is.na(CVD)&
!is.na(Depression) & WTMEC2YR > 0)
nhanes.mod421 <- nhanes.mod420 %>%
mutate(domain = nomiss & RIDRETH1=="4")
design.FST421 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod421)
design.FST.domain421 <- subset(design.FST421, domain)
library(gtsummary)
design.FST.domain421 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(RIDAGEYR, INDFMPIR, DMDBORN4_char, DMDEDUC2_char, DMDMARTL_char,
HSD010_char, PAQ605_char, PAQ620_char ,RHD280_char, DIQ010_char , MCQ010_char,
MCQ080_char, MCQ160A_char, MCQ220_char, RIDRETH1, Depression_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by CVD") %>%
bold_labels()
#Frequency Recheck
prop.table(svytable(~CVD_char + SLQ050_char, design.FST.domain121), margin = 2)
design.FST.domain121 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(MCQ160F_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status\n
(Females age 45y and older)") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain421)
confint(svymean(~RIDAGEYR, design.FST.domain421), df = degf(design.FST.domain421))
svymean( ~INDFMPIR, design.FST.domain421)
confint(svymean(~INDFMPIR, design.FST.domain421), df = degf(design.FST.domain421))
#final prevalence and CI
#95% ci for prevalence
svyby(~RIDRETH1_char,~CVD_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char,~DMDBORN4_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~DMDBORN4_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char,~DMDEDUC2, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~DMDEDUC2, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char, ~DMDMARTL_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~DMDMARTL_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD,~HSD010_char, design.FST.domain421, svymean)
confint(svyby(~CVD,~HSD010_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD,~PAQ605_char, design.FST.domain421, svymean)
confint(svyby(~CVD,~PAQ605_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char,~PAQ620_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~PAQ620_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char,~RHD280_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~RHD280_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char,~DIQ010_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~DIQ010_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char,~MCQ010_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~MCQ010_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char,~MCQ080_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~MCQ080_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char,~MCQ160A_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~MCQ160A_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char,~MCQ220_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~MCQ220_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~CVD_char,~Depression_char, design.FST.domain421, svymean)
confint(svyby(~CVD_char,~Depression_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
######## Hysterectomy new 2017-2018
nhanes.mod520 <- X2017_2018 %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when( RIDRETH1=="1"~"Maxican",RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDBORN4_char=case_when(DMDBORN4=="1"~"USA",DMDBORN4=="2"~"no"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " , DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree", DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married/Living with Partner" , DMDMARTL=="2"~"Widowed/Divorced/Separated" , DMDMARTL=="3"~ "Never married"),
HSD010_char=case_when(HSD010=="1"~"A",HSD010=="2"~"B",HSD010=="3"~"C", HSD010=="4"~"D",HSD010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
PAQ605_char=case_when(PAQ605=="1"~"yes", PAQ605=="2"~"no"),
PAQ620_char=case_when(PAQ620=="1"~"yes", PAQ620=="2"~"no"),
RHD280_char=case_when(RHD280=="1"~"yes", RHD280=="2"~"no"),
DIQ010_char = case_when(DIQ010 == "1" ~ "yes", DIQ010 == "2" ~ "No"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No"),
MCQ160A_char=case_when(MCQ160A == "1" ~ "yes",MCQ160A== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"),
Depression_char=case_when(Depression == "0" ~ "NO", Depression== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(HSD010) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(DMDMARTL) &
!is.na(DMDEDUC2) & !is.na(SLQ050) & !is.na(DIQ010) &
!is.na(RHD280) & !is.na(PAQ620) & !is.na(PAQ605) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160A) & !is.na(MCQ220)& !is.na(CVD)&
!is.na(Depression)& !is.na(SDMVPSU) & WTMEC2YR > 0)
nhanes.mod521 <- nhanes.mod520 %>%
mutate(domain = nomiss & RIDRETH1=="4")
design.FST521 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod521)
design.FST.domain521 <- subset(design.FST521, domain)
library(gtsummary)
design.FST.domain521 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(RIDAGEYR, INDFMPIR, DMDBORN4_char, DMDEDUC2_char, DMDMARTL_char,
HSD010_char, PAQ605_char, PAQ620_char ,RHD280_char, DIQ010_char , MCQ010_char,
MCQ080_char, MCQ160A_char, MCQ220_char, RIDRETH1, Depression_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by CVD") %>%
bold_labels()
#Frequency Recheck
prop.table(svytable(~CVD_char + SLQ050_char, design.FST.domain121), margin = 2)
design.FST.domain121 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c(MCQ160F_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by smoking status\n
(Females age 45y and older)") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain521)
confint(svymean(~RIDAGEYR, design.FST.domain521), df = degf(design.FST.domain521))
svymean( ~INDFMPIR, design.FST.domain521)
confint(svymean(~INDFMPIR, design.FST.domain521), df = degf(design.FST.domain521))
#final prevalence and CI
#95% ci for prevalence
svyby(~RIDRETH1_char,~CVD_char, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~RIDRETH1_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD,~DMDBORN4, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~DMDBORN4_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD_char,~DMDEDUC2, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~DMDEDUC2, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD_char, ~DMDMARTL_char, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~DMDMARTL_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD,~HSD010_char, design.FST.domain521, svymean)
confint(svyby(~CVD,~HSD010_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD,~PAQ605_char, design.FST.domain521, svymean)
confint(svyby(~CVD,~PAQ605_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD_char,~PAQ620_char, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~PAQ620_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD_char,~RHD280_char, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~RHD280_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD_char,~DIQ010_char, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~DIQ010_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD_char,~MCQ010_char, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~MCQ010_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD_char,~MCQ080_char, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~MCQ080_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD_char,~MCQ160A_char, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~MCQ160A_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD_char,~MCQ220_char, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~MCQ220_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
svyby(~CVD_char,~Depression_char, design.FST.domain521, svymean)
confint(svyby(~CVD_char,~Depression_char, design.FST.domain521, svymean),
df = degf(design.FST.domain521))
#combined new hysterectomy
nhanes.mod670 <- combined %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when( RIDRETH1=="1"~"Maxican",RIDRETH1=="2"~"other", RIDRETH1=="3"~"white", RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDBORN4_char=case_when(DMDBORN4=="1"~"USA",DMDBORN4=="2"~"no"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ", DMDEDUC2=="2"~"9-11th grade " , DMDEDUC2=="3"~ "High school graduate/GED or equivalent", DMDEDUC2=="4"~ "Some college or AA degree", DMDEDUC2=="5"~"College graduate or above"),
DMDMARTL_char=case_when(DMDMARTL=="1"~"Married/Living with Partner" , DMDMARTL=="2"~"Widowed/Divorced/Separated" , DMDMARTL=="3"~ "Never married"),
HSD010_char=case_when(HSD010=="1"~"A",HSD010=="2"~"B",HSD010=="3"~"C", HSD010=="4"~"D",HSD010=="5"~"E"),
SLQ050_char=case_when(SLQ050 == "1" ~ "yes", SLQ050 == "2" ~ "No") ,
PAQ605_char=case_when(PAQ605=="1"~"yes", PAQ605=="2"~"no"),
PAQ620_char=case_when(PAQ620=="1"~"yes", PAQ620=="2"~"no"),
RHD280_char=case_when(RHD280=="1"~"yes", RHD280=="2"~"no"),
DIQ010_char = case_when(DIQ010 == "1" ~ "yes", DIQ010 == "2" ~ "No"),
MCQ010_char=case_when(MCQ010 == "1" ~ "yes", MCQ010 == "2" ~ "No") ,
MCQ080_char=case_when(MCQ080== "1" ~ "Yes", MCQ080 == "2" ~ "No"),
MCQ160A_char=case_when(MCQ160A == "1" ~ "yes",MCQ160A== "2" ~ "No"),
MCQ220_char=case_when(MCQ220 == "1" ~ "yes", MCQ220== "2" ~ "No"),
CVD_char=case_when(CVD == "0" ~ "NO", CVD== "1" ~ "YES"),
Depression_char=case_when(Depression == "0" ~ "NO", Depression== "1" ~ "YES"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTME2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(HSD010) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(DMDMARTL) &
!is.na(DMDEDUC2) & !is.na(SLQ050) & !is.na(DIQ010) &
!is.na(RHD280) & !is.na(PAQ620) & !is.na(PAQ605) & !is.na(MCQ010)&
!is.na(MCQ080) & !is.na( MCQ160A) & !is.na(MCQ220)& !is.na(CVD)&
!is.na(Depression) & WTMEC2YR > 0)
nhanes.mod671 <- nhanes.mod670 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST671 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod671)
design.FST.domain671 <- subset(design.FST671, domain)
library(gtsummary)
design.FST.domain671 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = CVD_char,
# Use include to select variables
include = c( RIDRETH1_char, RIDAGEYR, INDFMPIR, DMDBORN4_char, DMDEDUC2_char, DMDMARTL_char,
HSD010_char, PAQ605_char, PAQ620_char ,RHD280_char, DIQ010_char , MCQ010_char,
MCQ080_char, MCQ160A_char, MCQ220_char, Depression_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Sample Size and Weighted Sample
Characteristics by CVD Status 2011-2012") %>%
bold_labels()
fit.ex9.1.domain671 <- svyglm(CVD ~ RIDRETH1_char+RIDAGEYR+ INDFMPIR+ DMDBORN4_char+
DMDEDUC2_char+DMDMARTL_char+
HSD010_char+ PAQ605_char+ PAQ620_char+
RHD280_char+ DIQ010_char+ MCQ010_char+
MCQ080_char+ MCQ160A_char+ MCQ220_char+ Depression_char,
family=gaussian(),
design=design.FST.domain671)
fit.ex9.1.domain671 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ220_char,
family=gaussian(),
design=design.FST.domain671)
fit.ex9.1.domain671 %>%
tbl_regression(intercept = T,
estimate_fun = function(x) style_sigfig(x, digits = 3),
pvalue_fun = function(x) style_pvalue(x, digits = 3),
label = list(
RIDAGEYR~"Year",
INDFMPIR ~"proverty",
DMDBORN4_char~"Bron US",
DMDEDUC2_char ~ "Education",
DMDMARTL_char ~ "Marital",
HSD010_char ~ "General Health",
PAQ605_char~"Moderate",
PAQ620_char~"vigious",
RHD280_char ~"hysterectomy"
DIQ010_char~ "diabetes",
MCQ010_char~ "Asthma",
MCQ080_char~ "overweight",
MCQ160A_char~"Arithritis"
MCQ220_char~ "cancer",
CVD_char~"chd",
Depression_char~"depression")) %>%
add_global_p(keep = T, test.statistic = "F") %>%
modify_caption("Weighted linear regression results for fasting glucose (mmol/L)\n
(Females age 45y and older)")
fit.ex671 <- svyglm(CVD ~ DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ220_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex671, df.resid=degf(fit.ex671$survey.design))
confint(fit.ex671, ddf.resid=degf(fit.ex671$survey.design))
OR.CI <- cbind(exp( coef(fit.ex671)),
exp(confint(fit.ex671,
df.resid=degf(fit.ex671$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex672 <- svyglm(CVD ~ RIDRETH1, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex672, df.resid=degf(fit.ex672$survey.design))
confint(fit.ex672, ddf.resid=degf(fit.ex672$survey.design))
OR.CI <- cbind(exp( coef(fit.ex672)),
exp(confint(fit.ex672,
df.resid=degf(fit.ex672$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex63 <- svyglm(CVD ~ DMDEDUC2_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex63, df.resid=degf(fit.ex63$survey.design))
confint(fit.ex63, ddf.resid=degf(fit.ex63$survey.design))
OR.CI <- cbind(exp( coef(fit.ex63)),
exp(confint(fit.ex63,
df.resid=degf(fit.ex63$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex64 <- svyglm(CVD ~ DMDMARTL_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex64, df.resid=degf(fit.ex64$survey.design))
confint(fit.ex64, ddf.resid=degf(fit.ex64$survey.design))
OR.CI <- cbind(exp( coef(fit.ex64)),
exp(confint(fit.ex64,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex65 <- svyglm(CVD ~
HSD010_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex65, df.resid=degf(fit.ex65$survey.design))
confint(fit.ex65, ddf.resid=degf(fit.ex65$survey.design))
OR.CI <- cbind(exp( coef(fit.ex65)),
exp(confint(fit.ex65,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex66 <- svyglm(CVD ~ SLQ050_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex66, df.resid=degf(fit.ex66$survey.design))
confint(fit.ex66, ddf.resid=degf(fit.ex66$survey.design))
OR.CI <- cbind(exp( coef(fit.ex66)),
exp(confint(fit.ex66,
df.resid=degf(fit.ex66$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex67 <- svyglm(CVD ~ DIQ010_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex67, df.resid=degf(fit.ex67$survey.design))
confint(fit.ex67, ddf.resid=degf(fit.ex67$survey.design))
OR.CI <- cbind(exp( coef(fit.ex67)),
exp(confint(fit.ex67,
df.resid=degf(fit.ex67$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex68 <- svyglm(CVD ~ MCQ010_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex68, df.resid=degf(fit.ex68$survey.design))
confint(fit.ex68, ddf.resid=degf(fit.ex68$survey.design))
OR.CI <- cbind(exp( coef(fit.ex68)),
exp(confint(fit.ex68,
df.resid=degf(fit.ex68$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex69 <- svyglm(CVD ~ MCQ080_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex69, df.resid=degf(fit.ex69$survey.design))
confint(fit.ex69, ddf.resid=degf(fit.ex69$survey.design))
OR.CI <- cbind(exp( coef(fit.ex69)),
exp(confint(fit.ex69,
df.resid=degf(fit.ex69$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex9 <- svyglm(CVD ~ Depression_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex9, df.resid=degf(fit.ex9$survey.design))
confint(fit.ex9, ddf.resid=degf(fit.ex9$survey.design))
OR.CI <- cbind(exp( coef(fit.ex9)),
exp(confint(fit.ex9,
df.resid=degf(fit.ex9$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex72 <- svyglm(CVD ~ MCQ220_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex72, df.resid=degf(fit.ex72$survey.design))
confint(fit.ex72, ddf.resid=degf(fit.ex72$survey.design))
OR.CI <- cbind(exp( coef(fit.ex72)),
exp(confint(fit.ex72,
df.resid=degf(fit.ex72$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex727 <- svyglm(CVD ~ DMDBORN4_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex727, df.resid=degf(fit.ex727$survey.design))
confint(fit.ex727, ddf.resid=degf(fit.ex727$survey.design))
OR.CI <- cbind(exp( coef(fit.ex727)),
exp(confint(fit.ex727,
df.resid=degf(fit.ex727$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex728 <- svyglm(CVD ~ PAQ605_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex728, df.resid=degf(fit.ex728$survey.design))
confint(fit.ex728, ddf.resid=degf(fit.ex728$survey.design))
OR.CI <- cbind(exp( coef(fit.ex728)),
exp(confint(fit.ex728,
df.resid=degf(fit.ex728$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex729 <- svyglm(CVD ~ PAQ620_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex729, df.resid=degf(fit.ex729$survey.design))
confint(fit.ex729, ddf.resid=degf(fit.ex729$survey.design))
OR.CI <- cbind(exp( coef(fit.ex729)),
exp(confint(fit.ex729,
df.resid=degf(fit.ex729$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex730 <- svyglm(CVD ~ RHD280_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex730, df.resid=degf(fit.ex730$survey.design))
confint(fit.ex730, ddf.resid=degf(fit.ex730$survey.design))
OR.CI <- cbind(exp( coef(fit.ex730)),
exp(confint(fit.ex730,
df.resid=degf(fit.ex730$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex731 <- svyglm(CVD ~ MCQ160A_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex731, df.resid=degf(fit.ex731$survey.design))
confint(fit.ex731, ddf.resid=degf(fit.ex731$survey.design))
OR.CI <- cbind(exp( coef(fit.ex731)),
exp(confint(fit.ex731,
df.resid=degf(fit.ex731$survey.design))))[-1,]
round(OR.CI, 3)
#vizulization practice
Year
Diabetes Asthma Overweight
Arthiritis Depression
install.packages("ggplot2")
library(ggplot2)
# turn-off scientific notation like 1e+48
options(scipen=999)
theme_set(theme_bw())
data('design.FST.domain671' , package = "ggplot2")
k1<-ggplot(vizualizations, aes(x = Year, y = Edu_College_graduate_or_above, group = 1)) +
geom_line() +
geom_point()
k2<-ggplot(vizualizations, aes(x = Year, y = Edu_Less_than_9th_Grade , group = 1)) +
geom_line() +
geom_point()
k3<-ggplot(vizualizations, aes(x = Year, y =Widowed_Divorced_Separated, group = 1)) +
geom_line() +
geom_point()
k4<-ggplot(vizualizations, aes(x = Year, y = Hysterectomy, group = 1)) +
geom_line() +
geom_point()
k5<-ggplot(vizualizations, aes(x = Year, y = Diabetes, group = 1)) +
geom_line() +
geom_point()
k6<-ggplot(vizualizations, aes(x = Year, y = Asthma, group = 1)) +
geom_line() +
geom_point()
k7<-ggplot(vizualizations, aes(x = Year, y = Overweight, group = 1)) +
geom_line() +
geom_point()
k8<-ggplot(vizualizations, aes(x = Year, y = Arthritis, group = 1)) +
geom_line() +
geom_point()
k9<-ggplot(vizualizations, aes(x = Year, y = Depression, group = 1)) +
geom_line() +
geom_point()
install.packages(' gridExtra')
library( gridExtra)
ggarrange(k1, k2,k3,k4,k5,k6,k7,k8,k9, ncol = 2, nrow = 5)
install.packages("ggpubr")
library(ggpubr)
plot<- ggarrange(k8,k9,k4,k5,k6,k7,k3,k2, ncol = 2, nrow = 4,
common.legend = TRUE,legend="bottom")
annotate_figure(plot, top = text_grob("Weighted Significant Trend prevalence(%) of CVD for Black African American Women ",
color = "red", face = "bold", size = 14))
Dental
install.packages(c('tidyverse', "survey"))
library(tidyverse)
library(survey)
library(gtsummary)
##########2017-2018
nhanes.mod120 <- Merge_data_1718_cleaned_%>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white",
RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ",
DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent",
DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DBQ700_char=case_when(DBQ700=="0"~"good",
DBQ700=="1"~"bad" ),
OHQ033_char=case_when(OHQ033=="1"~"A",
OHQ033=="2"~"B",
OHQ033=="3"~"C",
OHQ033=="4"~"D"),
RIAGENDR_char=case_when(RIAGENDR== "1" ~ "male",
RIAGENDR == "2" ~ "female") ,
OHQ770_char=case_when( OHQ770 == "1" ~ "yes",
OHQ770 == "2" ~ "No") ,
OHQ610_char = case_when(OHQ610 == "1" ~ "yes",
OHQ610 == "2" ~ "No"),
OHQ612_char=case_when(OHQ612 == "1" ~ "yes",
OHQ612 == "2" ~ "No") ,
OHQ614_char=case_when(OHQ614== "1" ~ "Yes",
OHQ614 == "2" ~ "No"),
OHQ620_char=case_when(OHQ620=="1"~"A",
OHQ620=="2"~"B",
OHQ620=="3"~"C",
OHQ620=="4"~"D",
OHQ620=="5"~"E"),
OHQ640_char=case_when(OHQ640=="1"~"A",
OHQ640=="2"~"B",
OHQ640=="3"~"C",
OHQ640=="4"~"D",
OHQ640=="5"~"E"),
OHQ680_char=case_when(OHQ680=="1"~"A",
OHQ680=="2"~"B",
OHQ680=="3"~"C",
OHQ680=="4"~"D",
OHQ680=="5"~"E"),
OHQ835_char=case_when(OHQ835 == "1" ~ "yes",
OHQ835== "2" ~ "No"),
OHQ845_char=case_when(OHQ845=="1"~"A",
OHQ845=="2"~"B",
OHQ845=="3"~"C",
OHQ845=="4"~"D",
OHQ845=="5"~"E"),
OHQ850_char=case_when(OHQ850 == "1" ~ "yes",
OHQ850== "2" ~ "No"),
OHQ860_char=case_when(OHQ860 == "1" ~ "yes",
OHQ860== "2" ~ "No"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(OHQ860) &!is.na(OHQ850) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(OHQ850) &
!is.na(DMDEDUC2) & !is.na(DBQ700) & !is.na(OHQ033) & !is.na(OHQ770)&
!is.na(OHQ610) & !is.na( OHQ612) & !is.na(OHQ614)& !is.na(OHQ620) &
!is.na(OHQ640)& !is.na(OHQ680) & !is.na( OHQ835) & !is.na(OHQ845) & WTMEC2YR > 0)
nhanes.mod121 <- nhanes.mod120 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST121 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod121)
design.FST.domain121 <- subset(design.FST121, domain)
library(gtsummary)
design.FST.domain121 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = DBQ700_char,
# Use include to select variables
include = c(RIAGENDR_char, RIDAGEYR, RIDRETH1_char, DMDEDUC2_char,
OHQ033_char, OHQ770_char, OHQ610_char, OHQ612_char, OHQ614_char,
OHQ620_char, OHQ640_char, OHQ680_char, OHQ835_char,
OHQ845_char, OHQ850_char, OHQ860_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by Races") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain121)
confint(svymean(~RIDAGEYR, design.FST.domain121),
df = degf(design.FST.domain121))
#final prevalence and CI
#95% ci for prevalence
svyby(~DBQ700_char,~RIDRETH1, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~RIDRETH1_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~DMDEDUC2_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~DMDEDUC2_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char, ~RIAGENDR_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char, ~ RIAGENDR_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ033_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ033_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ770_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ770_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ610_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ610_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ612_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ612_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ614, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ614, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ620_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ620_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ640, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ640, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ680_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ680_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ835_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ835_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ845_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ845_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ850_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ850_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
svyby(~DBQ700_char,~OHQ860_char, design.FST.domain121, svymean)
confint(svyby(~DBQ700_char,~OHQ860_char, design.FST.domain121, svymean),
df = degf(design.FST.domain121))
##########2015-2016
nhanes.mod220 <- Merge_data_1516_cleaned %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white",
RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ",
DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent",
DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DBQ700_char=case_when(DBQ700=="0"~"good",
DBQ700=="1"~"bad" ),
OHQ033_char=case_when(OHQ033=="1"~"A",
OHQ033=="2"~"B",
OHQ033=="3"~"C",
OHQ033=="4"~"D"),
RIAGENDR_char=case_when(RIAGENDR== "1" ~ "male",
RIAGENDR == "2" ~ "female") ,
OHQ770_char=case_when( OHQ770 == "1" ~ "yes",
OHQ770 == "2" ~ "No") ,
OHQ610_char = case_when(OHQ610 == "1" ~ "yes",
OHQ610 == "2" ~ "No"),
OHQ612_char=case_when(OHQ612 == "1" ~ "yes",
OHQ612 == "2" ~ "No") ,
OHQ614_char=case_when(OHQ614== "1" ~ "Yes",
OHQ614 == "2" ~ "No"),
OHQ620_char=case_when(OHQ620=="1"~"A",
OHQ620=="2"~"B",
OHQ620=="3"~"C",
OHQ620=="4"~"D",
OHQ620=="5"~"E"),
OHQ640_char=case_when(OHQ640=="1"~"A",
OHQ640=="2"~"B",
OHQ640=="3"~"C",
OHQ640=="4"~"D",
OHQ640=="5"~"E"),
OHQ680_char=case_when(OHQ680=="1"~"A",
OHQ680=="2"~"B",
OHQ680=="3"~"C",
OHQ680=="4"~"D",
OHQ680=="5"~"E"),
OHQ835_char=case_when(OHQ835 == "1" ~ "yes",
OHQ835== "2" ~ "No"),
OHQ845_char=case_when(OHQ845=="1"~"A",
OHQ845=="2"~"B",
OHQ845=="3"~"C",
OHQ845=="4"~"D",
OHQ845=="5"~"E"),
OHQ850_char=case_when(OHQ850 == "1" ~ "yes",
OHQ850== "2" ~ "No"),
OHQ860_char=case_when(OHQ860 == "1" ~ "yes",
OHQ860== "2" ~ "No"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(OHQ860) &!is.na(OHQ850) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(OHQ850) &
!is.na(DMDEDUC2) & !is.na(DBQ700) & !is.na(OHQ033) & !is.na(OHQ770)&
!is.na(OHQ610) & !is.na( OHQ612) & !is.na(OHQ614)& !is.na(OHQ620) &
!is.na(OHQ640)& !is.na(OHQ680) & !is.na( OHQ835) & !is.na(OHQ845) & WTMEC2YR > 0)
nhanes.mod221 <- nhanes.mod220 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST221 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod221)
design.FST.domain221 <- subset(design.FST221, domain)
library(gtsummary)
design.FST.domain221 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = DBQ700_char,
# Use include to select variables
include = c(RIAGENDR_char, RIDAGEYR, RIDRETH1_char, DMDEDUC2_char,
OHQ033_char, OHQ770_char, OHQ610_char, OHQ612_char, OHQ614_char,
OHQ620_char, OHQ640_char, OHQ680_char, OHQ835_char,
OHQ845_char, OHQ850_char, OHQ860_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by Races") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain221)
confint(svymean(~RIDAGEYR, design.FST.domain221),
df = degf(design.FST.domain221))
#final prevalence and CI
#95% ci for prevalence
svyby(~DBQ700_char,~RIDRETH1, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~RIDRETH1_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~DMDEDUC2_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~DMDEDUC2_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char, ~RIAGENDR_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char, ~ RIAGENDR_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~OHQ033_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~OHQ033_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~OHQ770_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~OHQ770_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~OHQ610_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~OHQ610_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~OHQ614_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~OHQ614_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~OHQ620_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~OHQ620_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~OHQ680_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~OHQ680_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~OHQ835_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~OHQ835_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~OHQ845_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~OHQ845_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~OHQ850_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~OHQ850_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
svyby(~DBQ700_char,~OHQ860_char, design.FST.domain221, svymean)
confint(svyby(~DBQ700_char,~OHQ860_char, design.FST.domain221, svymean),
df = degf(design.FST.domain221))
##########2013-2014
nhanes.mod320 <- Merge_data_1314_cleaned_ %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white",
RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ",
DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent",
DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DBQ700_char=case_when(DBQ700=="0"~"good",
DBQ700=="1"~"bad" ),
OHQ033_char=case_when(OHQ033=="1"~"A",
OHQ033=="2"~"B",
OHQ033=="3"~"C",
OHQ033=="4"~"D"),
RIAGENDR_char=case_when(RIAGENDR== "1" ~ "male",
RIAGENDR == "2" ~ "female") ,
OHQ770_char=case_when( OHQ770 == "1" ~ "yes",
OHQ770 == "2" ~ "No") ,
OHQ610_char = case_when(OHQ610 == "1" ~ "yes",
OHQ610 == "2" ~ "No"),
OHQ612_char=case_when(OHQ612 == "1" ~ "yes",
OHQ612 == "2" ~ "No") ,
OHQ614_char=case_when(OHQ614== "1" ~ "Yes",
OHQ614 == "2" ~ "No"),
OHQ620_char=case_when(OHQ620=="1"~"A",
OHQ620=="2"~"B",
OHQ620=="3"~"C",
OHQ620=="4"~"D",
OHQ620=="5"~"E"),
OHQ640_char=case_when(OHQ640=="1"~"A",
OHQ640=="2"~"B",
OHQ640=="3"~"C",
OHQ640=="4"~"D",
OHQ640=="5"~"E"),
OHQ680_char=case_when(OHQ680=="1"~"A",
OHQ680=="2"~"B",
OHQ680=="3"~"C",
OHQ680=="4"~"D",
OHQ680=="5"~"E"),
OHQ835_char=case_when(OHQ835 == "1" ~ "yes",
OHQ835== "2" ~ "No"),
OHQ845_char=case_when(OHQ845=="1"~"A",
OHQ845=="2"~"B",
OHQ845=="3"~"C",
OHQ845=="4"~"D",
OHQ845=="5"~"E"),
OHQ850_char=case_when(OHQ850 == "1" ~ "yes",
OHQ850== "2" ~ "No"),
OHQ860_char=case_when(OHQ860 == "1" ~ "yes",
OHQ860== "2" ~ "No"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(OHQ860) &!is.na(OHQ850) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(OHQ850) &
!is.na(DMDEDUC2) & !is.na(DBQ700) & !is.na(OHQ033) & !is.na(OHQ770)&
!is.na(OHQ610) & !is.na( OHQ612) & !is.na(OHQ614)& !is.na(OHQ620) &
!is.na(OHQ640)& !is.na(OHQ680) & !is.na( OHQ835) & !is.na(OHQ845) & WTMEC2YR > 0)
nhanes.mod321 <- nhanes.mod320 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST321 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod321)
design.FST.domain321 <- subset(design.FST321, domain)
library(gtsummary)
design.FST.domain321 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = DBQ700_char,
# Use include to select variables
include = c(RIAGENDR_char, RIDAGEYR, RIDRETH1_char, DMDEDUC2_char,
OHQ033_char, OHQ770_char, OHQ610_char, OHQ612_char, OHQ614_char,
OHQ620_char, OHQ640_char, OHQ680_char, OHQ835_char,
OHQ845_char, OHQ850_char, OHQ860_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by Races") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain321)
confint(svymean(~RIDAGEYR, design.FST.domain321),
df = degf(design.FST.domain321))
#final prevalence and CI
#95% ci for prevalence
svyby(~DBQ700_char,~RIDRETH1, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~RIDRETH1_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~DMDEDUC2_char, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~DMDEDUC2_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700,~DMDEDUC2, design.FST.domain321, svymean)
confint(svyby(~DBQ700,~DMDEDUC2, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char, ~RIAGENDR_char, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char, ~ RIAGENDR_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~OHQ033, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~OHQ033, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~OHQ770_char, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~OHQ770_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~OHQ610_char, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~OHQ610_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~OHQ614_char, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~OHQ614_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~OHQ620, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~OHQ620, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~OHQ680_char, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~OHQ680_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~OHQ835_char, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~OHQ835_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~OHQ845_char, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~OHQ845_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~OHQ850_char, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~OHQ850_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
svyby(~DBQ700_char,~OHQ860_char, design.FST.domain321, svymean)
confint(svyby(~DBQ700_char,~OHQ860_char, design.FST.domain321, svymean),
df = degf(design.FST.domain321))
##########2011-2012
nhanes.mod420 <- Merge_data_1112_cleaned%>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white",
RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ",
DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent",
DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DBQ700_char=case_when(DBQ700=="0"~"good",
DBQ700=="1"~"bad" ),
OHQ033_char=case_when(OHQ033=="1"~"A",
OHQ033=="2"~"B",
OHQ033=="3"~"C",
OHQ033=="4"~"D"),
RIAGENDR_char=case_when(RIAGENDR== "1" ~ "male",
RIAGENDR == "2" ~ "female") ,
OHQ770_char=case_when( OHQ770 == "1" ~ "yes",
OHQ770 == "2" ~ "No") ,
OHQ610_char = case_when(OHQ610 == "1" ~ "yes",
OHQ610 == "2" ~ "No"),
OHQ612_char=case_when(OHQ612 == "1" ~ "yes",
OHQ612 == "2" ~ "No") ,
OHQ614_char=case_when(OHQ614== "1" ~ "Yes",
OHQ614 == "2" ~ "No"),
OHQ620_char=case_when(OHQ620=="1"~"A",
OHQ620=="2"~"B",
OHQ620=="3"~"C",
OHQ620=="4"~"D",
OHQ620=="5"~"E"),
OHQ640_char=case_when(OHQ640=="1"~"A",
OHQ640=="2"~"B",
OHQ640=="3"~"C",
OHQ640=="4"~"D",
OHQ640=="5"~"E"),
OHQ680_char=case_when(OHQ680=="1"~"A",
OHQ680=="2"~"B",
OHQ680=="3"~"C",
OHQ680=="4"~"D",
OHQ680=="5"~"E"),
OHQ835_char=case_when(OHQ835 == "1" ~ "yes",
OHQ835== "2" ~ "No"),
OHQ845_char=case_when(OHQ845=="1"~"A",
OHQ845=="2"~"B",
OHQ845=="3"~"C",
OHQ845=="4"~"D",
OHQ845=="5"~"E"),
OHQ850_char=case_when(OHQ850 == "1" ~ "yes",
OHQ850== "2" ~ "No"),
OHQ860_char=case_when(OHQ860 == "1" ~ "yes",
OHQ860== "2" ~ "No"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(OHQ860) &!is.na(OHQ850) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(OHQ850) &
!is.na(DMDEDUC2) & !is.na(DBQ700) & !is.na(OHQ033) & !is.na(OHQ770)&
!is.na(OHQ610) & !is.na( OHQ612) & !is.na(OHQ614)& !is.na(OHQ620) &
!is.na(OHQ640)& !is.na(OHQ680) & !is.na( OHQ835) & !is.na(OHQ845) & WTMEC2YR > 0)
nhanes.mod421 <- nhanes.mod420 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST421 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod421)
design.FST.domain421 <- subset(design.FST421, domain)
library(gtsummary)
design.FST.domain421 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = DBQ700_char,
# Use include to select variables
include = c(RIAGENDR_char, RIDAGEYR, RIDRETH1_char, DMDEDUC2_char,
OHQ033_char, OHQ770_char, OHQ610_char, OHQ612_char, OHQ614_char,
OHQ620_char, OHQ640_char, OHQ680_char, OHQ835_char,
OHQ845_char, OHQ850_char, OHQ860_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Weighted descriptive statistics, by Races") %>%
bold_labels()
#mean value for continuous
svymean( ~RIDAGEYR, design.FST.domain421)
confint(svymean(~RIDAGEYR, design.FST.domain421),
df = degf(design.FST.domain421))
#final prevalence and CI
#95% ci for prevalence
svyby(~DBQ700_char,~RIDRETH1, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~RIDRETH1_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~DMDEDUC2, design.FST.domain421, svymean)
confint(svyby(I(CVD=="1"),~DMDEDUC2, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char, ~RIAGENDR_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char, ~ RIAGENDR_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ033_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ033_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ770_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ770_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ610_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ610_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ612_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ612_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ614_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ614_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ620_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ620_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ640_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ640_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ680_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ680_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ835_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ835_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ845, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ845, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ850_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ850_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
svyby(~DBQ700_char,~OHQ860_char, design.FST.domain421, svymean)
confint(svyby(~DBQ700_char,~OHQ860_char, design.FST.domain421, svymean),
df = degf(design.FST.domain421))
######combined
nhanes.mod670 <- Combined_ %>%
mutate(# Collapse race/ethnicity variable
# Create a character version for use as a by variable
# in tbl_svysummary, but need to change the values
# so they are in the correct order when alphabetized
RIDRETH1_char=case_when(RIDRETH1=="1"~"Maxican",
RIDRETH1=="2"~"other", RIDRETH1=="3"~"white",
RIDRETH1=="4"~"black", RIDRETH1=="5"~"multi"),
DMDEDUC2_char=case_when(DMDEDUC2=="1"~ "Less than 9th grade ",
DMDEDUC2=="2"~"9-11th grade " ,
DMDEDUC2=="3"~ "High school graduate/GED or equivalent",
DMDEDUC2=="4"~ "Some college or AA degree",
DMDEDUC2=="5"~"College graduate or above"),
DBQ700_char=case_when(DBQ700=="0"~"good",
DBQ700=="1"~"bad" ),
OHQ033_char=case_when(OHQ033=="1"~"A",
OHQ033=="2"~"B",
OHQ033=="3"~"C",
OHQ033=="4"~"D"),
RIAGENDR_char=case_when(RIAGENDR== "1" ~ "male",
RIAGENDR == "2" ~ "female") ,
OHQ770_char=case_when( OHQ770 == "1" ~ "yes",
OHQ770 == "2" ~ "No") ,
OHQ610_char = case_when(OHQ610 == "1" ~ "yes",
OHQ610 == "2" ~ "No"),
OHQ612_char=case_when(OHQ612 == "1" ~ "yes",
OHQ612 == "2" ~ "No") ,
OHQ614_char=case_when(OHQ614== "1" ~ "Yes",
OHQ614 == "2" ~ "No"),
OHQ620_char=case_when(OHQ620=="1"~"A",
OHQ620=="2"~"B",
OHQ620=="3"~"C",
OHQ620=="4"~"D",
OHQ620=="5"~"E"),
OHQ640_char=case_when(OHQ640=="1"~"A",
OHQ640=="2"~"B",
OHQ640=="3"~"C",
OHQ640=="4"~"D",
OHQ640=="5"~"E"),
OHQ680_char=case_when(OHQ680=="1"~"A",
OHQ680=="2"~"B",
OHQ680=="3"~"C",
OHQ680=="4"~"D",
OHQ680=="5"~"E"),
OHQ835_char=case_when(OHQ835 == "1" ~ "yes",
OHQ835== "2" ~ "No"),
OHQ845_char=case_when(OHQ845=="1"~"A",
OHQ845=="2"~"B",
OHQ845=="3"~"C",
OHQ845=="4"~"D",
OHQ845=="5"~"E"),
OHQ850_char=case_when(OHQ850 == "1" ~ "yes",
OHQ850== "2" ~ "No"),
OHQ860_char=case_when(OHQ860 == "1" ~ "yes",
OHQ860== "2" ~ "No"),
# Set missing fasting subsample weights to 0
# (the reason for this is explained below)
WTMEC2YR = case_when( is.na(WTMEC2YR) ~ 0,
!is.na(WTMEC2YR) ~ as.numeric(WTMEC2YR)),
# Complete case / non-zero weight indicator
# NOTE: This creates a logical vector, taking on values TRUE and FALSE
nomiss = !is.na(OHQ860) &!is.na(OHQ850) & !is.na(RIDRETH1) &
!is.na(RIDAGEYR) & !is.na(RIAGENDR) & !is.na(OHQ850) &
!is.na(DMDEDUC2) & !is.na(DBQ700) & !is.na(OHQ033) & !is.na(OHQ770)&
!is.na(OHQ610) & !is.na( OHQ612) & !is.na(OHQ614)& !is.na(OHQ620) &
!is.na(OHQ640)& !is.na(OHQ680) & !is.na( OHQ835) & !is.na(OHQ845) & WTMEC2YR > 0)
nhanes.mod671 <- nhanes.mod670 %>%
mutate(domain = nomiss & RIDRETH1 == "4")
design.FST671 <- svydesign(strata=~SDMVSTRA, id=~SDMVPSU, weights=~WTMEC2YR,
nest=TRUE, survey.lonely.psu = "adjust",
data=nhanes.mod671)
design.FST.domain671 <- subset(design.FST671, domain)
library(gtsummary)
design.FST.domain671 %>%
tbl_svysummary(
# Use a character variable here. A factor leads to an error
by = DBQ700_char,
# Use include to select variables
include = c(RIAGENDR_char, RIDAGEYR, RIDRETH1_char, DMDEDUC2_char,
OHQ033_char, OHQ770_char, OHQ610_char, OHQ612_char, OHQ614_char,
OHQ620_char, OHQ640_char, OHQ680_char, OHQ835_char,
OHQ845_char, OHQ850_char, OHQ860_char),
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0, 1))
) %>%
modify_header(label = "**Variable**",
all_stat_cols() ~ "**{level}**<br>N = {n} ({style_percent(p, digits=1)}%)") %>%
modify_caption("Sample Size and Weighted Sample
Characteristics by CVD Status Among Black African American with hysterectomy,
1999-2016") %>%
bold_labels()
RIAGENDR_char, RIDAGEYR, RIDRETH1_char, DMDEDUC2_char,
OHQ033_char, OHQ770_char, OHQ610_char, OHQ612_char, OHQ614_char,
OHQ620_char, OHQ640_char, OHQ680_char, OHQ835_char,
OHQ845_char, OHQ850_char, OHQ860_char
fit.ex60 <- svyglm(DBQ700 ~ DMDEDUC2_char,
family=quasibinomial(),
design=design.FST.domain671)
summary(fit.ex60, df.resid=degf(fit.ex60$survey.design))
confint(fit.ex60, ddf.resid=degf(fit.ex60$survey.design))
OR.CI <- cbind(exp( coef(fit.ex60)),
exp(confint(fit.ex60,
df.resid=degf(fit.ex60$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex9.1.domain671 <- svyglm(CVD ~ RIDRETH1+DMDEDUC2_char+DMDMARTL_char+
HSD010_char+SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ220_char,
family=gaussian(),
design=design.FST.domain671)
fit.ex9.1.domain671 %>%
tbl_regression(intercept = T,
estimate_fun = function(x) style_sigfig(x, digits = 3),
pvalue_fun = function(x) style_pvalue(x, digits = 3),
label = list(
DMDEDUC2_char ~ "Education",
DMDMARTL_char ~ "Marital",
HSD010_char ~ "General Health",
SLQ050_char ~ "Sleep",
DIQ160_char ~ "prediabetes",
MCQ010_char ~ "Asthma",
MCQ080_char ~ "overweight",
MCQ220_char ~ "cancer")) %>%
add_global_p(keep = T, test.statistic = "F") %>%
modify_caption("Weighted linear regression results for fasting glucose (mmol/L)\n
(Females age 45y and older)")
fit.ex671 <- svyglm(DBQ700~ RIAGENDR_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex671, df.resid=degf(fit.ex671$survey.design))
confint(fit.ex671, ddf.resid=degf(fit.ex671$survey.design))
OR.CI <- cbind(exp( coef(fit.ex671)),
exp(confint(fit.ex671,
df.resid=degf(fit.ex671$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex672 <- svyglm(DBQ700 ~ RIDRETH1_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex672, df.resid=degf(fit.ex672$survey.design))
confint(fit.ex672, ddf.resid=degf(fit.ex672$survey.design))
OR.CI <- cbind(exp( coef(fit.ex672)),
exp(confint(fit.ex672,
df.resid=degf(fit.ex672$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex63 <- svyglm(DBQ700 ~ OHQ033_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex63, df.resid=degf(fit.ex63$survey.design))
confint(fit.ex63, ddf.resid=degf(fit.ex63$survey.design))
OR.CI <- cbind(exp( coef(fit.ex63)),
exp(confint(fit.ex63,
df.resid=degf(fit.ex63$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex64 <- svyglm(DBQ700 ~ OHQ770_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex64, df.resid=degf(fit.ex64$survey.design))
confint(fit.ex64, ddf.resid=degf(fit.ex64$survey.design))
OR.CI <- cbind(exp( coef(fit.ex64)),
exp(confint(fit.ex64,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex65 <- svyglm(DBQ700 ~OHQ610_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex65, df.resid=degf(fit.ex65$survey.design))
confint(fit.ex65, ddf.resid=degf(fit.ex65$survey.design))
OR.CI <- cbind(exp( coef(fit.ex65)),
exp(confint(fit.ex65,
df.resid=degf(fit.ex64$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex66 <- svyglm(DBQ700 ~ OHQ612_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex66, df.resid=degf(fit.ex66$survey.design))
confint(fit.ex66, ddf.resid=degf(fit.ex66$survey.design))
OR.CI <- cbind(exp( coef(fit.ex66)),
exp(confint(fit.ex66,
df.resid=degf(fit.ex66$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex67 <- svyglm(DBQ700 ~ OHQ614_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex67, df.resid=degf(fit.ex67$survey.design))
confint(fit.ex67, ddf.resid=degf(fit.ex67$survey.design))
OR.CI <- cbind(exp( coef(fit.ex67)),
exp(confint(fit.ex67,
df.resid=degf(fit.ex67$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex68 <- svyglm(DBQ700 ~ OHQ620_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex68, df.resid=degf(fit.ex68$survey.design))
confint(fit.ex68, ddf.resid=degf(fit.ex68$survey.design))
OR.CI <- cbind(exp( coef(fit.ex68)),
exp(confint(fit.ex68,
df.resid=degf(fit.ex68$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex69 <- svyglm(DBQ700 ~ OHQ640_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex69, df.resid=degf(fit.ex69$survey.design))
confint(fit.ex69, ddf.resid=degf(fit.ex69$survey.design))
OR.CI <- cbind(exp( coef(fit.ex69)),
exp(confint(fit.ex69,
df.resid=degf(fit.ex69$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex72 <- svyglm(DBQ700 ~ OHQ680_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex72, df.resid=degf(fit.ex72$survey.design))
confint(fit.ex72, ddf.resid=degf(fit.ex72$survey.design))
OR.CI <- cbind(exp( coef(fit.ex72)),
exp(confint(fit.ex72,
df.resid=degf(fit.ex72$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex73 <- svyglm(DBQ700 ~ OHQ835_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex73, df.resid=degf(fit.ex73$survey.design))
confint(fit.ex73, ddf.resid=degf(fit.ex73$survey.design))
OR.CI <- cbind(exp( coef(fit.ex73)),
exp(confint(fit.ex73,
df.resid=degf(fit.ex73$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex73 <- svyglm(DBQ700 ~ OHQ845_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex73, df.resid=degf(fit.ex73$survey.design))
confint(fit.ex73, ddf.resid=degf(fit.ex73$survey.design))
OR.CI <- cbind(exp( coef(fit.ex73)),
exp(confint(fit.ex73,
df.resid=degf(fit.ex73$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex66 <- svyglm(DBQ700 ~ OHQ850_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex66, df.resid=degf(fit.ex66$survey.design))
confint(fit.ex66, ddf.resid=degf(fit.ex66$survey.design))
OR.CI <- cbind(exp( coef(fit.ex66)),
exp(confint(fit.ex66,
df.resid=degf(fit.ex66$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex67 <- svyglm(DBQ700 ~ OHQ860_char, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex67, df.resid=degf(fit.ex67$survey.design))
confint(fit.ex67, ddf.resid=degf(fit.ex67$survey.design))
OR.CI <- cbind(exp( coef(fit.ex67)),
exp(confint(fit.ex67,
df.resid=degf(fit.ex67$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex68 <- svyglm(DBQ700 ~ MCQ010_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex68, df.resid=degf(fit.ex68$survey.design))
confint(fit.ex68, ddf.resid=degf(fit.ex68$survey.design))
OR.CI <- cbind(exp( coef(fit.ex68)),
exp(confint(fit.ex68,
df.resid=degf(fit.ex68$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex69 <- svyglm(DBQ700 ~ MCQ080_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex69, df.resid=degf(fit.ex69$survey.design))
confint(fit.ex69, ddf.resid=degf(fit.ex69$survey.design))
OR.CI <- cbind(exp( coef(fit.ex69)),
exp(confint(fit.ex69,
df.resid=degf(fit.ex69$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex71 <- svyglm(DBQ700 ~ MCQ160F_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex71, df.resid=degf(fit.ex71$survey.design))
confint(fit.ex71, ddf.resid=degf(fit.ex71$survey.design))
OR.CI <- cbind(exp( coef(fit.ex71)),
exp(confint(fit.ex71,
df.resid=degf(fit.ex71$survey.design))))[-1,]
round(OR.CI, 3)
fit.ex72 <- svyglm(DBQ700 ~ SLQ050_char+
DIQ160_char+MCQ010_char+ MCQ080_char+
MCQ160F_char+ MCQ220_char+DMDEDUC2_char+ RIDAGEYR, family=quasibinomial(),
design = design.FST.domain671)
summary(fit.ex72, df.resid=degf(fit.ex72$survey.design))
confint(fit.ex72, ddf.resid=degf(fit.ex72$survey.design))
OR.CI <- cbind(exp( coef(fit.ex72)),
exp(confint(fit.ex72,
df.resid=degf(fit.ex72$survey.design))))[-1,]
round(OR.CI, 3)
#vizulization practice
install.packages("ggplot2")
library(ggplot2)
# turn-off scientific notation like 1e+48
options(scipen=999)
theme_set(theme_bw())
data('design.FST.domain671' , package = "ggplot2")
k1<-ggplot(vizualizations, aes(x = Black_female, y =Year , group = 1)) +
geom_line() +
geom_point()
k2<-ggplot(vizualizations, aes(x = for_wrong_bothering_or_hurting_last_dental_visit, y = Year, group = 1)) +
geom_line() +
geom_point()
k3<-ggplot(vizualizations, aes(x = Mean_age_poor_diet, y = Year, group = 1)) +
geom_line() +
geom_point()
k4<-ggplot(vizualizations, aes(x = need_dental_visit_but_couldnot_get_it, y = Year , group = 1)) +
geom_line() +
geom_point()
k5<-ggplot(vizualizations, aes(x = never_embarrassed_for_mouth, y = Year, group = 1)) +
geom_line() +
geom_point()
k6<-ggplot(vizualizations, aes(x = having_gum_disease, y = Year, group = 1)) +
geom_line() +
geom_point()
k7<-ggplot(vizualizations, aes(x = fair_teeth_and_gums, y =Year , group = 1)) +
geom_line() +
geom_point()
k8<-ggplot(vizualizations, aes(x = Poor_teeth_and_gums, y = Year , group = 1)) +
geom_line() +
geom_point()
k1<-ggplot(vizualizations, aes(x = Year, y = Black_female, group = 1)) +
geom_line() +
geom_point()
k2<-ggplot(vizualizations, aes(x = Year, y = for_wrong_bothering_or_hurting_last_dental_visit, group = 1)) +
geom_line() +
geom_point()
k3<-ggplot(vizualizations, aes(x = Year, y =Mean_age_poor_diet, group = 1)) +
geom_line() +
geom_point()
k4<-ggplot(vizualizations, aes(x = Year, y = need_dental_visit_but_couldnot_get_it , group = 1)) +
geom_line() +
geom_point()
k5<-ggplot(vizualizations, aes(x = Year, y = never_embarrassed_for_mouth, group = 1)) +
geom_line() +
geom_point()
k6<-ggplot(vizualizations, aes(x = Year, y = having_gum_disease, group = 1)) +
geom_line() +
geom_point()
k7<-ggplot(vizualizations, aes(x = Year, y = fair_teeth_and_gums, group = 1)) +
geom_line() +
geom_point()
k8<-ggplot(vizualizations, aes(x = Year, y = Poor_teeth_and_gums, group = 1)) +
geom_line() +
geom_point()
install.packages(' gridExtra')
library( gridExtra)
ggarrange(k1, k2,k3,k4,k5,k6,k7, ncol = 2, nrow = 4)
install.packages("ggpubr")
library(ggpubr)
plot<- ggarrange(k1, k2,k3,k4,k5,k6,k7,k8, ncol = 2, nrow = 4,
common.legend = TRUE,legend="bottom")
annotate_figure(plot, top = text_grob("Weighted Significant Trend prevalence(%) of oral health for poor diet among for Black African American ",
color = "red", face = "bold", size = 14))
plot
NHANES download
install.packages(c("colorspace", "nhanesA"))
library(colorspace)
library(nhanesA)
install.packages('openxlsx')
library(openxlsx)
options(timeout=360)
# Create Data Frame From Temporary File
download.file("https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/MCQ_G.XPT",
tf5 <- tempfile(), mode="wb")
Medical1112 <- foreign::read.xport(tf5)
demo12 <- demo1_2 [c("SEQN", "RIAGENDR", "RIDAGEYR", "DMDBORN",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
###2011-2012
demo11_12 <- nhanes('DEMO_G')
HEALTH12_12<-nhanes("HSQ_G")
Mental12_12<-nhanes("DPQ_G")
Sleep1112<-nhanes("SLQ_G")
Physical1112<-nhanes("PAQ_G")
Reproductive1112<-nhanes("RHQ_G")
Diabetes<-nhanes('DIQ_G')
Medical<-nhanes("MCQ_G")
merge11_12 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo11_12 ,HEALTH12_12,Mental12_12,Sleep1112,Physical1112,
Reproductive1112,
Diabetes,Medical1112))
write.xlsx(merge11_12,"C:/Users/Lenovo/Desktop/NHANES_2011_2012/Merge data 201112.xlsx",asTable = FALSE, overwrite = TRUE)
#####2013-2014
demo13_14 <- nhanes('DEMO_H')
HEALTH13_14<-nhanes("HSQ_H")
Mental13_14<-nhanes("DPQ_H")
Sleep1314<-nhanes("SLQ_H")
Physical1314<-nhanes("PAQ_H")
Reproductive1314<-nhanes("RHQ_H")
Diabetes1314<-nhanes('DIQ_H')
Medical1314<-nhanes("MCQ_H")
merge13_14 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo13_14 ,HEALTH13_14,Mental13_14,Sleep1314,
Physical1314,
Reproductive1314,
Diabetes1314,Medical1314))
write.xlsx(merge13_14,"C:/Users/Lenovo/Desktop/NHANES_2013_2014/Merge data 201314.xlsx",asTable = FALSE, overwrite = TRUE)
#####2015-2016
demo15_16 <- nhanes('DEMO_I')
HEALTH15_16<-nhanes("HSQ_I")
Mental15_16<-nhanes("DPQ_I")
Sleep1516<-nhanes("SLQ_I")
Physical1516<-nhanes("PAQ_I")
Reproductive1516<-nhanes("RHQ_I")
Diabetes1516<-nhanes('DIQ_I')
Medical1516<-nhanes("MCQ_I")
merge15_16 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo15_16 ,HEALTH15_16,Mental15_16,Sleep1516,
Physical1516,Reproductive1516,Diabetes1516,Medical1516))
write.xlsx(merge15_16,"C:/Users/Lenovo/Desktop/NHANES_2015_2016/Merge data 201516.xlsx",
asTable = FALSE, overwrite = TRUE)
#####2017-2018
demo17_18 <- nhanes('DEMO_J')
HEALTH17_18<-nhanes("HSQ_J")
Mental17_18<-nhanes("DPQ_J")
Sleep1718<-nhanes("SLQ_J")
Physical1718<-nhanes("PAQ_J")
Reproductive1718<-nhanes("RHQ_J")
Diabetes1718<-nhanes('DIQ_J')
Medical1718<-nhanes("MCQ_J")
merge17_18 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo17_18 ,HEALTH17_18,Mental17_18,Sleep1718,Physical1718,Reproductive1718,
Diabetes1718,Medical1718))
write.xlsx(merge17_18,"C:/Users/Lenovo/Desktop/NHANES_2017_2018/Merge data 201718.xlsx",
asTable = FALSE, overwrite = TRUE)
###2017-2020
merge17_20 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(DEMO_2017_2020 ,Current_Health_Status_2017_2020,Mental_Health_2017_2020,Sleep_Disorders_2017_2020,Physical_Activity_2017_2020,Reproductive_Health_2017_2020,
Diabetes_2017_2020,Medical_Conditions_2017_2020))
write.xlsx(merge17_20,"C:/Users/Lenovo/Desktop/NHANES_2017_2020/Merge data 201720.xlsx",
asTable = FALSE, overwrite = TRUE)
### Hearing Download
demo11_12 <- nhanes('DEMO_G')
hearing11_12<- nhanes('AUQ_G')
demo12 <- demo11_12 [c("SEQN", "RIAGENDR", "RIDAGEYR",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
hearing12 <- hearing11_12 [c("SEQN", "AUQ054")]
hearingmerge11_12 <- merge(demo12,hearing12, all.x=TRUE)
write.xlsx(hearingmerge11_12,"C:/Users/Lenovo/Desktop/Hearing Loss/Merge data 11_12.xlsx",
asTable = FALSE, overwrite = TRUE)
demo9_10 <- nhanes('DEMO_F')
hearing9_10<- nhanes('AUQ_F')
demo10 <- demo9_10[c("SEQN", "RIAGENDR", "RIDAGEYR",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
hearing10 <- hearing9_10 [c("SEQN", "AUQ131")]
hearingmerge9_10 <- merge(demo10,hearing10, all.x=TRUE)
write.xlsx(hearingmerge9_10,"C:/Users/Lenovo/Desktop/Hearing Loss/Merge data 9_10.xlsx",
asTable = FALSE, overwrite = TRUE)
demo15_16 <- nhanes('DEMO_I')
hearing15_16<- nhanes('AUQ_I')
demo16 <- demo15_16[c("SEQN", "RIAGENDR", "RIDAGEYR",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
hearing16 <- hearing15_16 [c("SEQN", "AUQ054")]
hearingmerge15_16 <- merge(demo16,hearing16, all.x=TRUE)
write.xlsx(hearingmerge15_16,"C:/Users/Lenovo/Desktop/Hearing Loss/Merge data 15_16.xlsx",
asTable = FALSE, overwrite = TRUE)
demo17_18 <- nhanes('DEMO_J')
hearing17_18<- nhanes('AUQ_J')
demo18 <- demo17_18[c("SEQN", "RIAGENDR", "RIDAGEYR",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
hearing18 <- hearing17_18 [c("SEQN", "AUQ054")]
hearingmerge17_18<-Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo18,hearing18))
hearingmerge17_18 <- merge(demo18,hearing18, all.x=TRUE)
write.xlsx(hearingmerge17_18,"C:/Users/Lenovo/Desktop/Hearing Loss/Merge data 17_18.xlsx",
asTable = FALSE, overwrite = TRUE)
#############################e-cigaratte
########2017-2018
demo17_18 <- nhanes('DEMO_J')
cigerate17_18<- nhanes('SMQ_J')
demo18 <- demo17_18[c("SEQN", "RIAGENDR", "RIDAGEYR",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
cigerate18 <- cigerate17_18[c("SEQN", "SMQ900")]
medical18 <- Medical1718[c("SEQN", "MCQ010", "MCQ080",
"MCQ160A", "MCQ160B", "MCQ160C", "MCQ160D",
"MCQ160E", "MCQ160F", "MCQ160M", "MCQ160G",
"MCQ160K", "MCQ160O" , "MCQ520", "MCQ550",
"MCQ203", "MCQ220")]
merge17_18 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo18 ,cigerate18,medical18))
write.xlsx(merge17_18,"C:/Users/Lenovo/Desktop/e cigarrate/Merge data 17_18.xlsx",
asTable = FALSE, overwrite = TRUE)
########2015-2016
demo15_16 <- nhanes('DEMO_I')
cigerate15_16<- nhanes('SMQ_I')
demo16 <- demo15_16[c("SEQN", "RIAGENDR", "RIDAGEYR",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
cigerate16 <- cigerate15_16[c("SEQN", "SMQ900")]
medical16 <- Medical1516[c("SEQN", "MCQ010", "MCQ080",
"MCQ160A", "MCQ160B", "MCQ160C", "MCQ160D",
"MCQ160E", "MCQ160F", "MCQ160M", "MCQ160G",
"MCQ160K", "MCQ160O",
"MCQ203", "MCQ220")]
merge15_16 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo16 ,cigerate16,medical16))
write.xlsx(merge15_16,"C:/Users/Lenovo/Desktop/e cigarrate/Merge data 1516.xlsx",
asTable = FALSE, overwrite = TRUE)
#######2015-2016
DRUG16<- nhanes('RXQ_RX_I')
merge17_18 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo18 ,drug_translate))
download.file("https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/RXQ_RX_J.XPT",
tf2 <- tempfile(), mode="wb")
drug_translate16 <- nhanesTranslate('RXQ_RX_J',
c('RXDDRUG', # Respondent sequence number
'RXDDRGID', 'RXDDAYS'),
data = DRUG)
write.xlsx(merge17_18,"C:/Users/Lenovo/Desktop/Drug/merged17_18.xlsx",
asTable = FALSE, overwrite = TRUE)
######3# Dental Health
#######2011-2012
demo11_12 <- nhanes('DEMO_G')
diet11_12<- nhanes('DBQ_G')
dental11_12<- nhanes('OHQ_G')
demo12 <- demo11_12[c("SEQN", "RIAGENDR", "RIDAGEYR",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
diet12 <- diet11_12[c("SEQN", "DBQ700")]
dental12 <- dental11_12[c("SEQN", "OHQ033", "OHQ770",
"OHQ610", "OHQ612", "OHQ614", "OHQ620",
"OHQ640", "OHQ680", "OHQ835", "OHQ845",
"OHQ850", "OHQ860")]
merge11_12 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo12 ,diet12,dental12))
write.xlsx(merge11_12,"C:/Users/Lenovo/Desktop/Dental/Merge data 1112.xlsx",
asTable = FALSE, overwrite = TRUE)
#### 2013-2014
demo13_14 <- nhanes('DEMO_H')
diet13_14<- nhanes('DBQ_H')
dental13_14<- nhanes('OHQ_H')
demo14 <- demo13_14[c("SEQN", "RIAGENDR", "RIDAGEYR",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
diet14 <- diet13_14[c("SEQN", "DBQ700")]
dental14 <- dental13_14[c("SEQN", "OHQ033", "OHQ770",
"OHQ610", "OHQ612", "OHQ614", "OHQ620",
"OHQ640", "OHQ680", "OHQ835", "OHQ845",
"OHQ850", "OHQ860")]
merge13_14 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo14 ,diet14,dental14))
write.xlsx(merge13_14,"C:/Users/Lenovo/Desktop/Dental/Merge data 1314.xlsx",
asTable = FALSE, overwrite = TRUE)
######2015-2016
demo15_16 <- nhanes('DEMO_I')
diet15_16<- nhanes('DBQ_I')
dental15_16<- nhanes('OHQ_I')
demo16 <- demo15_16[c("SEQN", "RIAGENDR", "RIDAGEYR",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
diet16 <- diet15_16[c("SEQN", "DBQ700")]
dental16 <- dental15_16[c("SEQN", "OHQ033", "OHQ770",
"OHQ610", "OHQ612", "OHQ614", "OHQ620",
"OHQ640", "OHQ680", "OHQ835", "OHQ845",
"OHQ850", "OHQ860")]
merge15_16 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo16 ,diet16,dental16))
write.xlsx(merge15_16,"C:/Users/Lenovo/Desktop/Dental/Merge data 1516.xlsx",
asTable = FALSE, overwrite = TRUE)
######2017-2018
demo17_18 <- nhanes('DEMO_J')
diet17_18<- nhanes('DBQ_J')
dental17_18<- nhanes('OHQ_J')
demo18 <- demo17_18[c("SEQN", "RIAGENDR", "RIDAGEYR",
"RIDRETH1", "DMDEDUC2", "DMDMARTL",
"WTMEC2YR", "SDMVPSU", "SDMVSTRA")]
diet18 <- diet17_18[c("SEQN", "DBQ700")]
dental18 <- dental17_18[c("SEQN", "OHQ033", "OHQ770",
"OHQ610", "OHQ612", "OHQ614", "OHQ620",
"OHQ640", "OHQ680", "OHQ835", "OHQ845",
"OHQ850", "OHQ860")]
merge17_18 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo18 ,diet18,dental18))
write.xlsx(merge17_18,"C:/Users/Lenovo/Desktop/Dental/Merge data 1718.xlsx",
asTable = FALSE, overwrite = TRUE)
#######Drug
########2017-2018
DRUG<- nhanes('RXQ_RX_J')
medical18<- nhanes('MCQ_J')
download.file("https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/RXQ_RX_J.XPT",
tf2 <- tempfile(), mode="wb")
drug_translate <- nhanesTranslate('RXQ_RX_J',
c('RXDDRUG', # Respondent sequence number
'RXDDRGID', 'RXDDAYS'),
data = DRUG)
merge17__18 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo18 ,drug_translate,medical18 ))
write.xlsx(merge17__18,"C:/Users/Lenovo/Desktop/Drug/merged17__18.xlsx",
asTable = FALSE, overwrite = TRUE)
#####2015-2016
DRUG16<- nhanes('RXQ_RX_I')
medical16<- nhanes('MCQ_I')
demo16<- nhanes('DEMO_I')
drug_translate16 <- nhanesTranslate('RXQ_RX_I',
c('RXDDRUG','RXDDRGID', 'RXDDAYS'),
data = DRUG16)
merge15__16 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo16 ,drug_translate16,medical16 ))
write.xlsx(merge15__16,"C:/Users/Lenovo/Desktop/Drug/merged15__16corrected.xlsx",
asTable = FALSE, overwrite = TRUE)
#####2013-2014
DRUG14<- nhanes('RXQ_RX_H')
medical14<- nhanes('MCQ_H')
demo14<- nhanes('DEMO_H')
drug_translate14 <- nhanesTranslate('RXQ_RX_H',
c('RXDDRUG','RXDDRGID', 'RXDDAYS'),
data = DRUG14)
merge13__14 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo14 ,drug_translate14,medical14 ))
write.xlsx(merge13__14,"C:/Users/Lenovo/Desktop/Drug/merged13__14.xlsx",
asTable = FALSE, overwrite = TRUE)
#####2011-2012
DRUG12<- nhanes('RXQ_RX_G')
medical12<- nhanes('MCQ_G')
demo12<- nhanes('DEMO_G')
drug_translate12 <- nhanesTranslate('RXQ_RX_G',
c('RXDDRUG','RXDDRGID', 'RXDDAYS'),
data = DRUG12)
merge11__12 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo12 ,drug_translate12,medical12 ))
write.xlsx(merge11__12,"C:/Users/Lenovo/Desktop/Drug/merged11__12.xlsx",
asTable = FALSE, overwrite = TRUE)
#####2009-2010
DRUG10<- nhanes('RXQ_RX_F')
medical10<- nhanes('MCQ_F')
demo10<- nhanes('DEMO_F')
drug_translate10 <- nhanesTranslate('RXQ_RX_F',
c('RXDDRUG','RXDDRGID', 'RXDDAYS'),
data = DRUG10)
merge9__10 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo10 ,drug_translate10,medical10 ))
write.xlsx(merge9__10,"C:/Users/Lenovo/Desktop/Drug/merged9__10.xlsx",
asTable = FALSE, overwrite = TRUE)
#####2007-2008
DRUG8<- nhanes('RXQ_RX_E')
medical8<- nhanes('MCQ_E')
demo8<- nhanes('DEMO_E')
drug_translate8 <- nhanesTranslate('RXQ_RX_E',
c('RXDDRUG','RXDDRGID', 'RXDDAYS'),
data = DRUG8)
merge7__8 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo8 ,drug_translate8,medical8 ))
write.xlsx(merge7__8,"C:/Users/Lenovo/Desktop/Drug/merged7__8.xlsx",
asTable = FALSE, overwrite = TRUE)
#######Dissertation
#####2017-18
alq18<- nhanes('ALQ_J')
auq18<- nhanes('AUQ_J')
bpq18<- nhanes('BPQ_J')
cdq18<- nhanes('CDQ_J')
cbq18<- nhanes('CBQ_J')
hsq18<- nhanes('HSQ_J')
deq18<- nhanes('DEQ_J')
diq18<- nhanes('DIQ_J')
dlq18<- nhanes('DLQ_J')
fsq18<- nhanes('FSQ_J')
hiq18<- nhanes('HIQ_J')
huq18<- nhanes('HUQ_J')
mcq18<- nhanes('MCQ_J')
dpq18<- nhanes('DPQ_J')
ohq18<- nhanes('OHQ_J')
osq18<- nhanes('OSQ_J')
paq18<- nhanes('PAQ_J')
rhq18<- nhanes('RHQ_J')
slq18<- nhanes('SLQ_J')
smq18<- nhanes('SMQ_J')
demo18<-nhanes('DEMO_J')
merge18 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo18 ,alq18, auq18, bpq18, cdq18, cbq18,
hsq18, deq18, diq18, dlq18, fsq18, hiq18, huq18,
mcq18, dpq18, ohq18, osq18, paq18, rhq18, slq18, smq18 ))
write.xlsx(merge18,"C:/Users/Lenovo/Desktop/Dissertation/merged18.xlsx",
asTable = FALSE, overwrite = TRUE)
####2015-2016
alq16<- nhanes('ALQ_I')
auq16<- nhanes('AUQ_I')
bpq16<- nhanes('BPQ_I')
cdq16<- nhanes('CDQ_I')
cbq16<- nhanes('CBQ_I')
hsq16<- nhanes('HSQ_I')
deq16<- nhanes('DEQ_I')
diq16<- nhanes('DIQ_I')
dlq16<- nhanes('DLQ_I')
fsq16<- nhanes('FSQ_I')
hiq16<- nhanes('HIQ_I')
huq16<- nhanes('HUQ_I')
mcq16<- nhanes('MCQ_I')
dpq16<- nhanes('DPQ_I')
ohq16<- nhanes('OHQ_I')
osq16<- nhanes('OSQ_I')
paq16<- nhanes('PAQ_I')
rhq16<- nhanes('RHQ_I')
slq16<- nhanes('SLQ_I')
smq16<- nhanes('SMQ_I')
demo16<-nhanes('DEMO_I')
merge16 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo16 ,alq16, auq16, bpq16, cdq16, cbq16,
hsq16, deq16, diq16, dlq16, fsq16, hiq16, huq16,
mcq16, dpq16, ohq16, paq16, rhq16, slq16, smq16 ))
write.xlsx(merge16,"C:/Users/Lenovo/Desktop/Dissertation/merged16.xlsx",
asTable = FALSE, overwrite = TRUE)
######2013-14
alq14<- nhanes('ALQ_H')
mcq14<- nhanes('MCQ_H')
merge14 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo14 ,alq14, bpq14, cdq14, cbq14, hsq14,
deq14, diq14, dlq14, fsq14, hiq14, huq14, mcq14,
dpq14, ohq14, osq14, paq14, rhq14, slq14, smq14))
write.xlsx(merge14,"C:/Users/Lenovo/Desktop/Dissertation/merged14.xlsx",
asTable = FALSE, overwrite = TRUE)
######2011-12
alq12<- nhanes('ALQ_G')
auq12<- nhanes('AUQ_G')
bpq12<- nhanes('BPQ_G')
cdq12<- nhanes('CDQ_G')
cbq12<- nhanes('CBQ_G')
hsq12<- nhanes('HSQ_G')
deq12<- nhanes('DEQ_G')
diq12<- nhanes('DIQ_G')
dlq12<- nhanes('DLQ_G')
fsq12<- nhanes('FSQ_G')
hiq12<- nhanes('HIQ_G')
huq12<- nhanes('HUQ_G')
mcq12<- nhanes('MCQ_G')
dpq12<- nhanes('DPQ_G')
ohq12<- nhanes('OHQ_G')
osq12<- nhanes('OSQ_G')
paq12<- nhanes('PAQ_G')
rhq12<- nhanes('RHQ_G')
slq12<- nhanes('SLQ_G')
smq12<- nhanes('SMQ_G')
demo12<-nhanes('DEMO_G')
merge12 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo12 ,alq12, auq12, bpq12, cdq12, cbq12,
hsq12, deq12, diq12, fsq12, hiq12, huq12,
mcq12, dpq12, ohq12, paq12, rhq12, slq12, smq12))
write.xlsx(merge12,"C:/Users/Lenovo/Desktop/Dissertation/merged12.xlsx",
asTable = FALSE, overwrite = TRUE)
######2009-10
alq10<- nhanes('ALQ_F')
auq10<- nhanes('AUQ_F')
bpq10<- nhanes('BPQ_F')
cdq10<- nhanes('CDQ_F')
cbq10<- nhanes('CBQ_F')
hsq10<- nhanes('HSQ_F')
deq10<- nhanes('DEQ_F')
diq10<- nhanes('DIQ_F')
dlq10<- nhanes('DLQ_F')
fsq10<- nhanes('FSQ_F')
hiq10<- nhanes('HIQ_F')
huq10<- nhanes('HUQ_F')
mcq10<- nhanes('MCQ_F')
dpq10<- nhanes('DPQ_F')
ohq10<- nhanes('OHQ_F')
osq10<- nhanes('OSQ_F')
paq10<- nhanes('PAQ_F')
rhq10<- nhanes('RHQ_F')
slq10<- nhanes('SLQ_F')
smq10<- nhanes('SMQ_F')
demo10<-nhanes('DEMO_F')
merge10 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo10 ,alq10, auq10, bpq10, cdq10, cbq10,
hsq10, deq10, diq10, fsq10, hiq10, huq10,
mcq10, dpq10, ohq10, osq10, paq10, rhq10, slq10, smq10))
write.xlsx(merge10,"C:/Users/Lenovo/Desktop/Dissertation/merged10.xlsx",
asTable = FALSE, overwrite = TRUE)
######2007-8
alq8<- nhanes('ALQ_E')
auq8<- nhanes('AUQ_E')
bpq8<- nhanes('BPQ_E')
cdq8<- nhanes('CDQ_E')
cbq8<- nhanes('CBQ_E')
hsq8<- nhanes('HSQ_E')
deq8<- nhanes('DEQ_E')
diq8<- nhanes('DIQ_E')
dlq8<- nhanes('DLQ_E')
fsq8<- nhanes('FSQ_E')
hiq8<- nhanes('HIQ_E')
huq8<- nhanes('HUQ_E')
mcq8<- nhanes('MCQ_E')
dpq8<- nhanes('DPQ_E')
ohq8<- nhanes('OHQ_E')
osq8<- nhanes('OSQ_E')
paq8<- nhanes('PAQ_E')
rhq8<- nhanes('RHQ_E')
slq8<- nhanes('SLQ_E')
smq8<- nhanes('SMQ_E')
demo8<-nhanes('DEMO_E')
merge8 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo8 ,alq8, auq8, bpq8, cdq8, cbq8,
hsq8, diq8, fsq8, hiq8, huq8,
mcq8, dpq8, ohq8, osq8, paq8, rhq8, slq8, smq8))
write.xlsx(merge8,"C:/Users/Lenovo/Desktop/Dissertation/merged8.xlsx",
asTable = FALSE, overwrite = TRUE)
######2005-6
alq6<- nhanes('ALQ_D')
auq6<- nhanes('AUQ_D')
bpq6<- nhanes('BPQ_D')
cdq6<- nhanes('CDQ_D')
cbq6<- nhanes('CBQ_D')
hsq6<- nhanes('HSQ_D')
deq6<- nhanes('DEQ_D')
diq6<- nhanes('DIQ_D')
dlq6<- nhanes('DLQ_D')
fsq6<- nhanes('FSQ_D')
hiq6<- nhanes('HIQ_D')
huq6<- nhanes('HUQ_D')
mcq6<- nhanes('MCQ_D')
dpq6<- nhanes('DPQ_D')
ohq6<- nhanes('OHQ_D')
osq6<- nhanes('OSQ_D')
paq6<- nhanes('PAQ_D')
rhq6<- nhanes('RHQ_D')
slq6<- nhanes('SLQ_D')
smq6<- nhanes('SMQ_D')
demo6<-nhanes('DEMO_D')
merge6 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo6 ,alq6, auq6, bpq6, cdq6,
hsq6, deq6, diq6, fsq6, hiq6, huq6,
mcq6, dpq6, ohq6, osq6, paq6, rhq6, slq6, smq6))
write.xlsx(merge6,"C:/Users/Lenovo/Desktop/Dissertation/merged6.xlsx",
asTable = FALSE, overwrite = TRUE)
######2003-4
alq4<- nhanes('ALQ_C')
auq4<- nhanes('AUQ_C')
bpq4<- nhanes('BPQ_C')
cdq4<- nhanes('CDQ_C')
cbq4<- nhanes('CBQ_C')
hsq4<- nhanes('HSQ_C')
deq4<- nhanes('DEQ_C')
diq4<- nhanes('DIQ_C')
dlq4<- nhanes('DLQ_C')
fsq4<- nhanes('FSQ_C')
hiq4<- nhanes('HIQ_C')
huq4<- nhanes('HUQ_C')
mcq4<- nhanes('MCQ_C')
dpq4<- nhanes('DPQ_C')
ohq4<- nhanes('OHQ_C')
osq4<- nhanes('OSQ_C')
paq4<- nhanes('PAQ_C')
rhq4<- nhanes('RHQ_C')
slq4<- nhanes('SLQ_C')
smq4<- nhanes('SMQ_C')
demo4<-nhanes('DEMO_C')
merge4 <- Reduce(function(x,y) merge(x,y,by="SEQN",all=TRUE) ,
list(demo4 ,alq4, auq4, bpq4, cdq4,
hsq4, deq4, diq4, fsq4, hiq4, huq4,
mcq4, ohq4, osq4, paq4, rhq4, smq4))
write.xlsx(merge4,"C:/Users/Lenovo/Desktop/Dissertation/merged4.xlsx",
asTable = FALSE, overwrite = TRUE)
Vaccine CMC
install.packages("ggplot2")
library(ggplot2)
install.packages("gridExtra")
library(gridExtra)
install.packages("ggpubr")
library(ggpubr)
p1<-ggplot(Data, aes(x=Age_cat, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p2<-ggplot(Data, aes(x=Gender, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p3<-ggplot(Data, aes(x=Residence, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p4<-ggplot(Data, aes(x=Occupation, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p5<-ggplot(Data, aes(x=Smoker, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p6<-ggplot(Data, aes(x=Severity, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
plot<- ggarrange(p1, p2,p3,p4,p5,p6, ncol = 2, nrow = 3,
common.legend = TRUE,legend="bottom")
annotate_figure(plot, top = text_grob("Demographic variable improved status by Vacine status ",
color = "red", face = "bold", size = 14))
p11<-ggplot(Data, aes(x=Oxygen_mask, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p21<-ggplot(Data, aes(x=HFNC, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p31<-ggplot(Data, aes(x=NIV, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p41<-ggplot(Data, aes(x=Mechanical_ventilation, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
plot1<- ggarrange(p11, p21,p31,p41, ncol = 2, nrow = 2,
common.legend = TRUE,legend="bottom")
annotate_figure(plot1, top = text_grob("Using Oxygen Therapy and improved status by Vacine status ",
color = "red", face = "bold", size = 14))
p111<-ggplot(Data, aes(x=Home_Treatment, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p211<-ggplot(Data, aes(x=COVID_ward, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p311<-ggplot(Data, aes(x=HDU, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p411<-ggplot(Data, aes(x=ICU, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
plot11<- ggarrange(p111, p211,p311,p411, ncol = 2, nrow = 2,
common.legend = TRUE,legend="bottom")
annotate_figure(plot11, top = text_grob("Site of Treatment and improved status by Vacine status ",
color = "red", face = "bold", size = 14))
p51<-ggplot(Data, aes(x=vitamins, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p52<-ggplot(Data, aes(x=Paracetamol, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p53<-ggplot(Data, aes(x=Anti_histamin, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p54<-ggplot(Data, aes(x=Anti_viral, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p55<-ggplot(Data, aes(x=Azithromycin, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p56<-ggplot(Data, aes(x=Meropenem_Ceftriaxon, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p57<-ggplot(Data, aes(x=STEROID, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p58<-ggplot(Data, aes(x=Oral_Anticoagulants, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p59<-ggplot(Data, aes(x=Enoxaperin, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p60<-ggplot(Data, aes(x=Tocilizumab, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p61<-ggplot(Data, aes(x=Plasma, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
plot5<- ggarrange(p51, p52,p53,p54,p55, p56,p57, p58, p59,p60, p61, ncol = 2, nrow = 6,
common.legend = TRUE,legend="bottom")
annotate_figure(plot5, top = text_grob("Treatments and improved status by Vacine status ",
color = "red", face = "bold", size = 14))
p61<-ggplot(Data, aes(x=DIABETES, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p62<-ggplot(Data, aes(x=HYPERTENSION, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p63<-ggplot(Data, aes(x=IHD, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p64<-ggplot(Data, aes(x=COPD, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p65<-ggplot(Data, aes(x=BA, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p66<-ggplot(Data, aes(x=CANCER, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p67<-ggplot(Data, aes(x=CKD, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p68<-ggplot(Data, aes(x=ILD, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p69<-ggplot(Data, aes(x=CVD, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p70<-ggplot(Data, aes(x=CLD, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p71<-ggplot(Data, aes(x=Comorbidity, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
plot6<- ggarrange(p61, p62,p63,p64,p65, p66,p67, p68, p69,p70, p71, ncol = 2, nrow = 6,
common.legend = TRUE,legend="bottom")
annotate_figure(plot6, top = text_grob("Comorbidities and improved status by Vacine status ",
color = "red", face = "bold", size = 14))
p81<-ggplot(Data, aes(x=Asymptomatic, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p82<-ggplot(Data, aes(x=Rhinorrhoea, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p83<-ggplot(Data, aes(x=Insomnia, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p84<-ggplot(Data, aes(x=Lethargy_weakness, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p85<-ggplot(Data, aes(x=Chest_pain, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p86<-ggplot(Data, aes(x=Myalgia, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p87<-ggplot(Data, aes(x=Fever, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p88<-ggplot(Data, aes(x=Cough, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p89<-ggplot(Data, aes(x=Dyspnoea, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p90<-ggplot(Data, aes(x=Anosmia, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p91<-ggplot(Data, aes(x=Ageusia_Dysgeusia, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p92<-ggplot(Data, aes(x=Diarrhoea, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
plot8<- ggarrange(p81, p82,p83,p84,p85, p86,p87, p88, p89,p90, p91,p92, ncol = 2, nrow = 6,
common.legend = TRUE,legend="bottom")
annotate_figure(plot8, top = text_grob("Initial Presentations and improved status by Vacine status",
color = "red", face = "bold", size = 14))
p811<-ggplot(Data, aes(x=wbc, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p821<-ggplot(Data, aes(x=Platelet, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p831<-ggplot(Data, aes(x=Lymphocyte, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p841<-ggplot(Data, aes(x=Neutrophil, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p851<-ggplot(Data, aes(x=CRP, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p861<-ggplot(Data, aes(x=Ferritin, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p871<-ggplot(Data, aes(x=D_dimer, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p881<-ggplot(Data, aes(x=Chest_X_ray, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
p891<-ggplot(Data, aes(x=Chest_HRCT, y=Improved, fill=Vaccine_status)) +
geom_bar(stat="identity")+theme_minimal()
plot81<- ggarrange(p811, p821,p831,p841,p851, p861,p871, p891, ncol = 2, nrow = 4,
common.legend = TRUE,legend="bottom")
annotate_figure(plot81, top = text_grob("INVESTIGATION and improved status by Vacine status ",
color = "red", face = "bold", size = 14))
####
install.packages("glm")
library(glm)
help(lm)
lm(DR_1$overall~ DR_1$Gender)
mod2<-lm(DR_1$overall~ DR_1$Marital_Status)
summary(mod2)