Doubly robust estimator of risk in the presence of censoring dependent on time-varying covariates: application to a primary prevention trial for coronary events with pravastatin.

ABSTRACT:

Background

In the presence of dependent censoring even after stratification of baseline covariates, the Kaplan-Meier estimator provides an inconsistent estimate of risk. To account for dependent censoring, time-varying covariates can be used along with two statistical methods: the inverse probability of censoring weighted (IPCW) Kaplan-Meier estimator and the parametric g-formula estimator. The consistency of the IPCW Kaplan-Meier estimator depends on the correctness of the model specification of censoring hazard, whereas that of the parametric g-formula estimator depends on the correctness of the models for event hazard and time-varying covariates.

Methods

We combined the IPCW Kaplan-Meier estimator and the parametric g-formula estimator into a doubly robust estimator that

SUBMITTER: Kawahara T 

PROVIDER: S-EPMC7395418 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature