Parametric Estimation of the Mean Number of Events in the Presence of Competing Risks.

ABSTRACT: Recurrent events, for example, hospitalizations or drug prescriptions, are common in time-to-event research. One useful summary measure of the recurrent event process is the mean number of events. Methods for estimating the mean number of events exist and are readily implemented for situations in which the recurrent event is the only possible outcome. However, estimation gets more challenging in the competing risk setting, in which methods are so far limited to nonparametric approaches. To this end, we propose a postestimation command for estimating the mean number of events in the presence of competing risks by jointly modeling the intensity function of the recurrent event and the survival function for the competing events. The proposed method is implemented in the R-package JointFPM whic

SUBMITTER: Entrop JP 

PROVIDER: S-EPMC11836554 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature