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Multiple imputation of missing data in nested case-control and case-cohort studies.

Ruth H Keogh1, Shaun R Seaman2, Jonathan W Bartlett3

  • 1Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.

Biometrics
|June 6, 2018
PubMed
Summary
This summary is machine-generated.

This study adapts multiple imputation (MI) methods for missing covariate data in nested case-control and case-cohort substudies. These methods offer substantial efficiency gains, especially when using full-cohort data for imputation.

Keywords:
Case-cohort studyCohort studyCox proportional hazardsMissing dataMultiple imputationNested case-control study

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Nested case-control and case-cohort designs are common for cohort substudies.
  • Handling missing covariate data is crucial for the validity of these designs.
  • Existing multiple imputation (MI) methods primarily focus on full-cohort studies.

Purpose of the Study:

  • To adapt and evaluate multiple imputation (MI) methods for missing covariate data in nested case-control and case-cohort substudies.
  • To compare the performance of different MI approaches, including those using full-cohort data and substudy data only.
  • To assess the efficiency gains and robustness of the adapted MI methods.

Main Methods:

  • Adaptation of two MI methods: MI-approx and MI-SMC.
  • Application of the MI matched set approach for nested case-control studies.
  • Investigation through simulation studies and illustration with the ARIC Study cohort.

Main Results:

  • All investigated MI methods perform well when their assumptions are met.
  • Significant efficiency gains are achievable by imputing data missing by design using full-cohort data.
  • Imputing data missing by chance using substudy data also yields efficiency gains.
  • An intermediate approach offers greater efficiency than substudy-only imputation and improved robustness.

Conclusions:

  • Adapted MI methods effectively handle missing covariates in nested case-control and case-cohort studies.
  • Utilizing full-cohort data for imputation, especially for data missing by design, enhances study efficiency.
  • The intermediate MI approach provides a balance of efficiency and robustness for substudy analyses.