Novel Methods
Multilevel Spatiotemporal Models
Temporal patterns of hospitalizations over the life span on dialysis, where poor outcomes are typically exacerbated during the first year on dialysis, variations in hospitalizations among dialysis facilities across the U.S. contribute to spatial variation. Utilizing USRDS data, we propose a novel multilevel spatiotemporal functional model to study spatiotemporal patterns of hospitalization rates among dialysis facilities. Hospitalization rates of dialysis facilities are considered as spatially nested functional data with longitudinal hospitalizations nested in dialysis facilities and dialysis facilities nested in geographic regions. A multilevel Karhunen-Loeve expansion is utilized to model the two-level (facility and region) functional data, where spatial correlations are induced among region-specific principal component scores accounting for regional variation. Figure: Raw and predicted hospitalization rates at one month after transition to dialysis.
Publications
Qian Q, Nguyen DV, Telesca D, Kurum E, Rhee CM, Banerjee S, Li Y, Senturk D (2023) Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in dialysis patients. Biostatistics, in-press.
Li Y, Nguyen DV, Kurum E, Rhee CM, Banerjee S, Senturk D (2022) Multilevel varying coefficient spatiotemporal model, Stat, 11(1):e438.
Li Y, Nguyen DV, Banerjee S, Rhee CM, Kalantar-Zadeh K, Kurum E, Senturk D (2021) Multilevel modeling of spatially nested functional data: Spatiotemporal patterns of hospitalization rates in the US dialysis population. Statistics in Medicine, 40(17):3937-3952.
Profiling/Assessment of Dialysis Facilities
Profiling analysis aims to evaluate health care providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. Various common patient outcomes of interest include hospital-wide or cause-specific in-hospital mortality and 30-day unplanned hospital readmission. For example, the Center for Medicare and Medicaid Services (CMS) has implement various measures of quality of care at Hospital Compare. Our research focuses on elucidating profiling models (e.g., fixed and random effects models, time-dynamic profiling models) for various patient outcomes, with applications to assessment of US dialysis facilities. Fig. Time-dynamic profiling of dialysis facilities for 30-day hospital readmission using novel metric SDRR(t).
Publications
Estes JP, Chen Y, Senturk D, Rhee CM, Kurum E, You AS, Streja E, Kalantar-Zadeh K, Nguyen DV (2020) Profiling dialysis facilities for adverse recurrent events. Statistics in Medicine, 39:9, 1374-1389.
Estes JP, Nguyen DV, Chen Y, Dalrymple LS, Rhee CM, Kalantar-Zedeh K, Senturk D (2018) Time-dynamic profiling with application to hospital readmission among patients on dialysis (with discussion). Biometrics, Dec;74(4):1383-1394.
Estes JP, Nguyen DV, Chen Y, Dalrymple LS, Rhee CM, Kalantar-Zedeh K, Senturk D (2018) Rejoinder: Time-dynamic profiling with application to hospital readmission among patients on dialysis. Biometrics, Dec;74(4):1383-1394.
Chen Y, Rhee CM, Senturk D, Kurum E, Campos LF, Li Y, Kalantar-Zadeh K, Nguyen DV (2019) Association of U.S. dialysis facility staffing with profiling of hospital-wide 30-day unplanned readmission. Kidney Diseases, 5(3):153-162.
Chen Y, Senturk D, Estes JP, Campos LF, Rhee CM, Dalrymple LS, Kalantar-Zadeh K, Nguyen DV (2021) Performance characteristics of profiling methods and the impact of inadequate case-mix adjustment. Communications in Statistics – Simulation and Computation, in-press.
Senturk D, Chen Y, Estes JP, Campos LF, Rhee CM, Kalantar-Zadeh K, Nguyen DV (2020) Impact of case-mix measurement error on estimation and inference in profiling of health care providers. Communications in Statistics – Simulation and Computation, 49:8, 2206-2224.
Models for Time-Varying Effects
Methods to assess the effects of risk factors associated with patient outcome, where the effects are not constant, but are dynamic and vary over the duration of follow-up time or by patient characteristics (e.g., age, disease severity) are important for longitudinal studies. For example, understanding how cardiovascular (CV) risk evolves during the course of dialysis treatment and how it changes following critical events like infection-related hospitalization may inform better patient care. These methods will also allow identification of the time periods of increased outcome (e.g., CV) risk; this knowledge is potentially useful for formulation of CV risk reduction strategies. Generalized varying coefficient models (GVCMs) depart from traditional simplistic modeling approaches that assume a static or constant effect size for risk factors, e.g., "patients with baseline diabetes have 20% increased CV outcome risk." Clearly, although such a simplification is useful in some studies, it cannot be used to quantify how the effects of individual risk factors vary depending on age at the start of dialysis, for instance. Our group and collaborators have developed novel GVCMs and extensions that allow for flexible models of time varying effects.
Publications
Li Y, Nguyen DV, Kurum E, Rhee CM, Chen Y, Kalantar-Zadeh K, Senturk D (2020) A multilevel mixed effects varying coefficient model with multilevel predictors and random effects for modeling hospitalization risk in patients on dialysis. Biometrics, 76(3), 924-938.
Li Y, Nguyen DV, Chen Y, Rhee CM, Kalantar-Zedeh, Senturk D (2018) Modeling time-varying effects of multilevel risk factors of hospitalizations in patients on dialysis. Statistics in Medicine, 30;37(30):4707-4720.
Estes JP, Nguyen DV, Dalrymple LS, Mu Y, Senturk D (2016) Time-varying effect modeling with longitudinal data truncated by death: Conditional models, interpretations and inference. Statistics in Medicine, 35(11):1834-47.
Estes J, Nguyen DV, Dalrymple DS, Mu Y, Senturk D (2014) Cardiovascular event risk dynamics over time in older patients on dialysis: A generalized multiple-index varying coefficient model approach. Biometrics, 70, 754–764.
Senturk D, Ghosh S, Nguyen DV (2014) Exploratory time varying lagged regression: Modeling association of cognitive and functional trajectories with expected clinic visits in older adults. Computational Statistics and Data Analysis, 73, 1-15.
Senturk D, Dalrymple DS, Mohammed SM, Kaysen GA, Nguyen DV (2013) Modeling time varying effects with generalized and unsynchronized longitudinal data. Statistics in Medicine, 32, 2971-2987.
Senturk D, Nguyen DV (2011) Varying coefficient models for sparse noise-contaminated longitudinal data. Statistica Sinica, 21, 1831-1856.
Multilevel Joint Modeling
We develop novel multilevel joint models (MJMs) that accounts for three-level hierarchical data, with longitudinal measurements, hospitalizations over time, nested within subjects and subjects further nested within dialysis facilities where they receive regular care. MJM accommodates the hierarchical structure of the data from the USRDS, through multilevel random effects and multilevel risk factors affecting both survival and longitudinal hospitalization outcomes. At the subject level, these include patient demographics and baseline comorbidities. At the facility level, facility staffing, such as the ratio of nurse to patients, may impact patient outcomes.
Publications
Kurum E, Nguyen DV, Banerjee S, Li Y, Rhee CM, Senturk D (2022) A Bayesian multilevel time-varying framework for joint modeling of hospitalization and survival in patients on dialysis. Statistics in Medicine, 41(29): 5597-5611.
Kurum E, Nguyen DV, Li Y, Rhee CM, Kalantar-Zadeh K, Senturk D (2021) Multilevel joint models of hospitalization and survival in patients on dialysis, Stat, 10:e356 (p1-13).
Self-Controlled Case Series Method, Exposure Onset Error
The self-controlled case series (SCCS) method is an approach to study the relationship between time-varying exposures and adverse events (AEs), such as AEs following vaccination or other acute exposures. The SCCS design requires only subjects with one or more events. This aspect of the SCCS design is particularly useful for large longitudinal database applications. Another major advantage of the SCCS method is that it controls for all measured and unmeasured baseline confounders and is self-matched. Thus, the SCCS estimate of the relative incidence of events is not confounded by baseline differences in individual factors, such as socioeconomic status, underlying genetics, and baseline health status or comorbidities, which are difficult to accurately ascertain between exposure groups (e.g., vaccinated and unvaccinated individuals; patients on dialysis who do and do not acquire infections). Our work in this areas currently focuses on extending the SCCS method to studies where the exposure onset time (e.g., infection time) is not known precisely. We refer to this as "exposure onset measurement error."
Publications
Campos LF, Senturk D, Chen Y, Nguyen DV (2017) Bias and estimation under misspecification of the risk period in self-controlled case series studies. Stat, 6(1), 373-389 DOI: 10.1002/sta4.166.
Mohammed SM, Dalrymple DS, Senturk D, Nguyen DV (2013) Naïve hypothesis testing for case series models with time-varying exposure onset measurement error: Inference for infection-cardiovascular risk in patients on dialysis. Biometrics, 69, 520-529.
Mohammed SM, Dalrymple DS, Senturk D, Nguyen DV (2013) Design considerations for case series models with exposure onset measurement error. Statistics in Medicine, 28, 772-786.
*** ONLINE TOOL for study design and sample size calculation: Explore tool
Mohammed SM, Senturk D, Dalrymple DS, Nguyen DV (2012) Measurement error case series models with application to infection-cardiovascular risk in older patients on dialysis. Journal of the American Statistical Association, 107, 1310-1323.
Dalrymple, LS, Mohammed SM, Mu Y, Johansen KL, Chertow GM, Grimes B, Kaysen GA, Nguyen DV (2011) The risk of cardiovascular-related events following infection-related hospitalizations in older patients on dialysis. Clinical Journal of the American Society of Nephrology, 6, 1708-1713.
Development and Validation of Patient Outcome Risk Prediction
Risk prediction tools to inform patients, heath care providers, and researchers on the success of a treatment (e.g., surgery, kidney transplant, extra-corporeal membrane oxygenation [ECMO]) is an important component of the planning and initiation of treatment. Risk prediction tools, rigorously developed and validated, allow for more informed decision making at the individual patient level. Our group and collaborators have developed prediction tools for diverse patient populations, including patients on dialysis and neonates with congenital diaphragmatic hernia (CDH).
Publications
Obi Y, Nguyen DV, Zhou H, Soohoo M, Zhang L, Chen Y, Streja E, Sim JJ, Molnar MZ, Rhee CM, Abbott KC, Jacobsen SJ, Kovesdy CP, Kalantar-Zadeh K (2018) Development and validation of prediction scores for early mortality upon transition to dialysis. Mayo Clinic Proceedings, 93(9):1224-1235.
===> ONLINE PREDICTION for early mortality upon transition to dialysis: Explore DialysisScore
Molnar MZ, Nguyen DV, Chen Y, Ravel V, Streja E, Krishnan M, Kovesdy CP, Mehrotra R, Kalantar-Zadeh K (2017) Predictive score for post-transplantation outcomes. Transplantation, 101(6):1353-1364.
===> ONLINE PREDICTION for mortality and graft failure risk for dialysis patients: Explore transplantscore.com
Regression Models for Joint Modeling of Disease Onset and Recurrence, Zero-Inflated Count Data
Cardiovascular disease remains one of the leading causes of hospitalization and death in the population of patients on dialysis. Our aim here is to develop methods to jointly model the relationship/association between covariates and (a) the probability of cardiovascular events (onset), a binary process and (b) the rate of events (recurrence) once the realization is positive - when the ‘hurdle’ is crossed - using a zero-truncated Poisson distribution. When the observation period or follow-up time, from the start of dialysis, varies among individuals the estimated probability of positive cardiovascular events during the study period will be biased. We develop strategies to eliminate this bias. In the context of zero-inflated count data, we are also developing functional linear model to be able to model functional predictors X(t) measured over time t, for instance.
Publications
Senturk D, Dalrymple DS, Mu Y, Nguyen DV (2014) Weighted hurdle regression method for joint modeling of cardiovascular events likelihood and rate in the U.S. dialysis population. Statistics in Medicine, 33(25):4387-4401.
Senturk D, Dalrymple LS, Nguyen DV (2014) Functional linear models for zero-inflated count data with application to modeling hospitalizations in patients on dialysis. Statistics in Medicine, 33(27):4825-4840.