Semiparametric frailty models for zero-inflated event count data in the presence of informative dropout.

ABSTRACT: Recurrent events data are commonly encountered in medical studies. In many applications, only the number of events during the follow-up period rather than the recurrent event times is available. Two important challenges arise in such studies: (a) a substantial portion of subjects may not experience the event, and (b) we may not observe the event count for the entire study period due to informative dropout. To address the first challenge, we assume that underlying population consists of two subpopulations: a subpopulation nonsusceptible to the event of interest and a subpopulation susceptible to the event of interest. In the susceptible subpopulation, the event count is assumed to follow a Poisson distribution given the follow-up time and the subject-specific characteristics. We then introd

SUBMITTER: Diao G 

PROVIDER: S-EPMC7573886 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature