Multiple imputation for cause-specific Cox models: Assessing methods for estimation and prediction.

ABSTRACT: In studies analyzing competing time-to-event outcomes, interest often lies in both estimating the effects of baseline covariates on the cause-specific hazards and predicting cumulative incidence functions. When missing values occur in these baseline covariates, they may be discarded as part of a complete-case analysis or multiply imputed. In the latter case, the imputations may be performed either compatibly with a substantive model pre-specified as a cause-specific Cox model [substantive model compatible fully conditional specification (SMC-FCS)], or approximately so [multivariate imputation by chained equations (MICE)]. In a large simulation study, we assessed the performance of these three different methods in terms of estimating cause-specific regression coefficients and predicting cum

SUBMITTER: Bonneville EF 

PROVIDER: S-EPMC9523822 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature