Generalized accelerated recurrence time model for multivariate recurrent event data with missing event type.

ABSTRACT: Recurrent events data are frequently encountered in biomedical follow-up studies. The generalized accelerated recurrence time (GART) model (Sun et al., 2016), which formulates covariate effects on the time scale of the mean function of recurrent events (i.e., time to expected frequency), has arisen as a useful secondary analysis tool to provide meaningful physical interpretations. In this article, we investigate the GART model in a multivariate recurrent events setting, where subjects may experience multiple types of recurrent events and some event types may be missing. We propose methods for the GART model that utilize the inverse probability weighting technique or the estimating equation projection strategy to handle event types that are missing at random. The new methods do not require

SUBMITTER: Ma H 

PROVIDER: S-EPMC6085173 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature