ArticleStatistical methods in medical research2022
Multiple imputation for cause-specific Cox models: Assessing methods for estimation and prediction.
Article in Statistical methods in medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Predictive averaging and Rubin's rule-based model pooling to predict survival risk with imputations in the presence of missing patient data: methodology and verification using two case studies and simulations.BMC medical research methodology · 2026Article
- Pregnancy identification method as a source of bias in studies of prenatal exposures using real-world data.American journal of epidemiology · 2026Article
- Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood.Statistics in medicine · 2026Article
- Multiple imputation for missing values in ordinal variables from cancer registry data when performing Cox proportional hazards regression.BMC medical research methodology · 2026Article
- Oil spill cleanup related exposures to benzene, toluene, ethylbenzene, xylenes, and n-hexane and incident diabetes mellitus.Environmental research · 2025Article
- Multiple Imputation of Missing Covariates When Using the Fine-Gray Model.Statistics in medicine · 2025Article
- Impact of Depression at HIV PrEP Initiation on Sustained PrEP Care Engagement Among US Gay and Bisexual Men.Journal of acquired immune deficiency syndromes (1999) · 2025Article
- Development and external validation of prediction algorithms to improve early diagnosis of cancer.Nature communications · 2025Article
- Analyzing Coarsened and Missing Data by Imputation Methods.Statistics in medicine · 2025Article
- Multiple imputation with competing risk outcomes.Computational statistics · 2025Article
- Development and validation of a new algorithm for improved cardiovascular risk prediction.Nature medicine · 2024Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
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.
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