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 th

SUBMITTER: Estes JP 

PROVIDER: S-EPMC4828268 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature