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

Statistical Methods in Medical Research, vol. 31, pp. 1860–1880

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.

Authors 5

1. Edouard F. Bonneville corresponding Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

2. Matthieu Resche‐Rigon

   Inserm · Sorbonne Paris Cité · Hôpital Saint-Louis · Centre de Recherche Épidémiologie et Statistique

   Affiliation as printed

   Centre de Recherche en Epidémiologie et Statistiques Sorbonne Paris Cité, Paris, France

   ECSTRRA Team, INSERM, Paris, France

   Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, Paris, France

3. Johannes Schetelig

   University Hospital Carl Gustav Carus · Deutsche Knochenmarkspenderdatei

   Affiliation as printed

   DKMS Clinical Trials Unit, Dresden, Germany

   Dresden University Hospital, Dresden, Germany

4. Hein Putter Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

5. Liesbeth C. de Wreede Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center · Deutsche Knochenmarkspenderdatei

   Affiliation as printed

   DKMS Clinical Trials Unit, Dresden, Germany

   Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands