Multiple imputation in Cox regression when there are time-varying effects of covariates.

ABSTRACT: In Cox regression, it is important to test the proportional hazards assumption and sometimes of interest in itself to study time-varying effects (TVEs) of covariates. TVEs can be investigated with log hazard ratios modelled as a function of time. Missing data on covariates are common and multiple imputation is a popular approach to handling this to avoid the potential bias and efficiency loss resulting from a "complete-case" analysis. Two multiple imputation methods have been proposed for when the substantive model is a Cox proportional hazards regression: an approximate method (Imputing missing covariate values for the Cox model in Statistics in Medicine (2009) by White and Royston) and a substantive-model-compatible method (Multiple imputation of covariates by fully conditional specifica

SUBMITTER: Keogh RH 

PROVIDER: S-EPMC6220767 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature