title: Time-varying effect modeling with longitudinal data truncated by death: conditional models, interpretations, and inference.
abstract: Recent studies found that infection-related hospitalization was associated with increased risk of cardiovascular (CV) events, such as myocardial infarction and stroke in the dialysis population. In this work, we develop time-varying effects modeling tools in order to examine the CV outcome risk trajectories during the time periods before and after an initial infection-related hospitalization. For this, we propose partly conditional and fully conditional partially linear generalized varying coefficient models (PL-GVCMs) for modeling time-varying effects in longitudinal data with substantial follow-up truncation by death. Unconditional models that implicitly target an immortal population is not a relevant target of inference in applications involving a population with high mortality, like the dialysis population. A partly conditional model characterizes the outcome trajectory for the dynamic cohort of survivors, where each point in the longitudinal trajectory represents a snapshot of the population relationships among subjects who are alive at that time point. In contrast, a fully conditional approach models the time-varying effects of the population stratified by the actual time of death, where the mean response characterizes individual trends in each cohort stratum. We compare and contrast partly and fully conditional PL-GVCMs in our aforementioned application using hospitalization data from the United States Renal Data System. For inference, we develop generalized likelihood ratio tests. Simulation studies examine the efficacy of estimation and inference procedures.
authors.affiliation: Department of Biostatistics, University of California, Los Angeles, 90095, California, U.S.A.
authors.forename: Jason P
authors.initials: JP
authors.lastname: Estes
authors.affiliation: Department of Medicine, UC Irvine School of Medicine, Orange, 92868-3298, California, U.S.A.|Institute for Clinical and Translational Science, University of California, Irvine, 92687-1385, California, U.S.A.
authors.forename: Danh V
authors.initials: DV
authors.lastname: Nguyen
authors.affiliation: Division of Nephrology, Department of Medicine, University of California, Sacramento, 95817, California, U.S.A.
authors.forename: Lorien S
authors.initials: LS
authors.lastname: Dalrymple
authors.affiliation: Graduate Group in Epidemiology, University of California, Davis, 95616, California, U.S.A.
authors.forename: Yi
authors.initials: Y
authors.lastname: Mu
authors.affiliation: Department of Biostatistics, University of California, Los Angeles, 90095, California, U.S.A.|Department of Statistics, University of California, Los Angeles, 90095, California, U.S.A.
authors.forename: Damla
authors.initials: D
authors.lastname: Şentürk
mesh_terms: D000368:Aged; D002318:Cardiovascular Diseases / Q000209:etiology* / Q000401:mortality; D003198:Computer Simulation; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007239:Infections / Q000209:etiology* / Q000401:mortality*; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D006435:Renal Dialysis / Q000009:adverse effects* / Q000401:mortality*; D012307:Risk Factors; D014481:United States / Q000453:epidemiology
keywords: United States Renal Data System; cardiovascular outcomes; end-stage renal disease; fully conditional model; partially linear generalized varying coefficient models; time-varying effects