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

• Published in: Statistical methods in medical research Vol. 31; no. 10; pp. 1860 - 1880
• Main Authors: Bonneville, Edouard F, Resche-Rigon, Matthieu, Schetelig, Johannes, Putter, Hein, de Wreede, Liesbeth C
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.10.2022
• Subjects: Computer Simulation; Data Interpretation, Statistical; Models, Statistical; Proportional Hazards Models; multiple imputation; Competing risks; Cox model; substantive model compatible imputation; cause-specific hazards; missing covariates
• ISSN: 0962-2802; 1477-0334
• DOI: 10.1177/09622802221102623

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.