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Multiple imputation for cause-specific Cox models: Assessing methods for estimation and prediction.
- Source :
-
Statistical Methods in Medical Research . Oct2022, Vol. 31 Issue 10, p1860-1880. 21p. - Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09622802
- Volume :
- 31
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Statistical Methods in Medical Research
- Publication Type :
- Academic Journal
- Accession number :
- 159437854
- Full Text :
- https://doi.org/10.1177/09622802221102623