Dataset Information
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.
Ontology highlight
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 can adjust for dependent censoring. The estimator is theoretically more robust to model misspecification than the IPCW Kaplan-Meier estimator and the parametric g-formula estimator. We conducted simulation studies with a time-varying covariate that affected both time-to-event and censoring under correct and incorrect models for censoring, event, and time-varying covariates. We applied our proposed estimator to a large clinical trial data with censoring before the end of follow-up.Results
Simulation studies demonstrated that our proposed estimator is doubly robust, namely it is consistent if either the model for the IPCW Kaplan-Meier estimator or the models for the parametric g-formula estimator, but not necessarily both, is correctly specified. Simulation studies and data application demonstrated that our estimator can be more efficient than the IPCW Kaplan-Meier estimator.Conclusions
The proposed estimator is useful for estimation of risk if censoring is affected by time-varying risk factors.
SUBMITTER: Kawahara T
PROVIDER: S-EPMC7395418 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
Publications
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.
BMC medical research methodology 20200731 1
<h4>Background</h4>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 specifi ...[more]