Introduction to R and RStudio
1. Installation and setting of R and RStudio.
2. Overview of R programming language.
3. Introduction to RStudio interface (Console, Scripts, Environment/History, Plots etc.)
4. Basic syntax of R programming language and commands.
5. Installation and management of packages in R
Data Import and Management
6. Importing data into R (CSV, Excel, SQL, etc.).
7. Data cleaning and manipulation (handling missing data, outliers, transforming variables).
8. Data types in R (vectors, matrices, data frames, lists).
9. Data sub-setting and filtering.
Basic Statistical Analysis
10. Descriptive statistics (mean, median, mode, standard deviation).
11. Statistical tests: t-tests, chi-square, ANOVA, correlation.
12. Visualizing data: Histograms, box plots, scatter plots, bar charts.
Epidemiological Data Analysis
13. Epidemiological measures: Incidence, prevalence, risk ratios, odds ratios.
14. Cohort study analysis: Relative risk, hazard ratios.
15. Case-control study analysis: Odds ratios.
16. Confounding, interaction, and stratification.
Regression Analysis in Epidemiology
17. Simple and multiple linear regression.
18. Logistic regression for binary outcomes.
19. Poisson regression for count data.
20. Survival analysis: Kaplan-Meier curve, Cox proportional hazards model.
21. Model diagnostics and evaluation.
Handling Time Series and Longitudinal Data
22. Introduction to time series analysis in epidemiology.
23. Handling longitudinal data (repeated measures, mixed models).
Epidemiological Study Design and Analysis
24. Cross-sectional, cohort, and case-control studies.
25. Bias and confounding: How to detect and adjust.
26. Power analysis and sample size estimation.
27. Propensity score matching and adjustment.