Time-varying effect modeling with longitudinal data truncated by death

Conditional models, interpretations, and inference

Jason P. Estes, Danh V. Nguyen, Lorien Dalrymple, Yi Mu, Damla Şentürk

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
JournalStatistics in Medicine
DOIs
StateAccepted/In press - 2015

Fingerprint

Conditional Model
Longitudinal Data
Time-varying
Modeling
Varying Coefficient Model
Hospitalization
Population
Trajectory
Infection
Dialysis
Generalized Likelihood Ratio Test
Myocardial Infarction
Target
Health Services Needs and Demand
Snapshot
Stroke
Information Systems
Mortality
Truncation
Survivors

Keywords

  • Cardiovascular outcomes
  • End-stage renal disease
  • Fully conditional model
  • Partially linear generalized varying coefficient models
  • Time-varying effects
  • United States Renal Data System

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

Time-varying effect modeling with longitudinal data truncated by death : Conditional models, interpretations, and inference. / Estes, Jason P.; Nguyen, Danh V.; Dalrymple, Lorien; Mu, Yi; Şentürk, Damla.

In: Statistics in Medicine, 2015.

Research output: Contribution to journalArticle

Estes, Jason P. ; Nguyen, Danh V. ; Dalrymple, Lorien ; Mu, Yi ; Şentürk, Damla. / Time-varying effect modeling with longitudinal data truncated by death : Conditional models, interpretations, and inference. In: Statistics in Medicine. 2015.
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