Multiple imputation for cause-specific Cox models: Assessing methods for estimation and prediction. (October 2022)

• Record Type: Journal Article
• Authors: Bonneville, Edouard F; Resche-Rigon, Matthieu; Schetelig, Johannes; Putter, Hein; de Wreede, Liesbeth C
• Is Part Of: Statistical methods in medical research. Volume 31:Number 10(2022)
• Page Start: 1860
• Page End: 1880
• Publication Date: 2022-10
• Subjects: Competing risks -- cause-specific hazards -- multiple imputation -- missing covariates -- substantive model compatible imputation -- Cox model; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802221102623
• Languages: English
• ISSNs: 0962-2802

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 cumulative incidence functions. Concerning regression coefficients, results provide further support for use of SMC-FCS over MICE, particularly when covariate effects are large and the baseline hazards of the competing events are substantially different. Complete-case analysis also shows adequate performance in settings where missingness is not outcome dependent. With regard to cumulative incidence prediction, SMC-FCS and MICE are performed more similarly, as also evidenced in the illustrative analysis of competing outcomes following a hematopoietic stem cell transplantation. The findings are discussed alongside recommendations for practising statisticians.