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

Edouard F Bonneville1, Matthieu Resche-Rigon2,3,4, Johannes Schetelig5,6

  • 1Department of Biomedical Data Sciences, 4501Leiden University Medical Center, Leiden, The Netherlands.

Statistical Methods in Medical Research
|June 6, 2022
PubMed
Summary
This summary is machine-generated.

When analyzing competing risks, missing covariate data can be handled by complete-case analysis or multiple imputation. Substantive model compatible fully conditional specification (SMC-FCS) imputation generally outperforms multivariate imputation by chained equations (MICE) for estimating cause-specific hazards.

Keywords:
Competing risksCox modelcause-specific hazardsmissing covariatesmultiple imputationsubstantive model compatible imputation

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Competing time-to-event outcomes require estimating cause-specific hazards and cumulative incidence functions.
  • Missing baseline covariates pose challenges, often addressed by complete-case analysis or multiple imputation.
  • Imputation methods include substantive model compatible fully conditional specification (SMC-FCS) and multivariate imputation by chained equations (MICE).

Purpose of the Study:

  • To evaluate the performance of complete-case analysis, SMC-FCS, and MICE.
  • To compare methods for estimating cause-specific regression coefficients.
  • To assess methods for predicting cumulative incidence functions in the presence of missing covariate data.

Main Methods:

  • A large-scale simulation study was conducted.
  • Performance was assessed for estimating cause-specific regression coefficients.
  • Performance was evaluated for predicting cumulative incidence functions.

Main Results:

  • SMC-FCS demonstrated superior performance over MICE for regression coefficient estimation, especially with large covariate effects and differing baseline hazards.
  • Complete-case analysis showed adequate performance when missingness was not outcome-dependent.
  • SMC-FCS and MICE exhibited similar performance in cumulative incidence prediction.

Conclusions:

  • SMC-FCS is recommended over MICE for estimating cause-specific hazards, particularly under specific conditions.
  • Complete-case analysis is a viable option when missingness is unrelated to the outcome.
  • Both imputation methods performed comparably for cumulative incidence prediction, as shown in a hematopoietic stem cell transplantation example.