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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Published on: January 8, 2020

Using full-cohort data in nested case-control and case-cohort studies by multiple imputation.

Ruth H Keogh1, Ian R White

  • 1MRC Biostatistics Unit, Cambridge, U.K.; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, U.K. ruth.keogh@lshtm.ac.uk

Statistics in Medicine
|April 25, 2013
PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) enhances efficiency in nested case-control and case-cohort studies by utilizing full cohort data. This method is particularly effective when surrogate exposure data is available in the complete cohort.

Keywords:
case-cohort studymultiple imputationnested case-control studyrejection sampling

Related Experiment Videos

Last Updated: May 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Large prospective cohort studies often face limitations in obtaining expensive exposure measurements for all participants.
  • Exposure-disease association studies frequently rely on nested case-control or case-cohort designs, collecting complete data only from sampled individuals.
  • Full cohorts may contain valuable, inexpensive covariate data and potential exposure surrogates that are often underutilized.

Purpose of the Study:

  • To propose and evaluate a multiple imputation (MI) approach for leveraging full cohort data in the analysis of nested case-control and case-cohort studies.
  • To address the issue of missing exposure data in sub-studies by treating the full cohort as a dataset with missing values.
  • To enhance the statistical efficiency of exposure-disease association studies by incorporating all available cohort information.

Main Methods:

  • Utilizing fully observed data from the entire cohort to fit imputation models.
  • Employing multiple imputation (MI) techniques to analyze data from nested case-control and case-cohort sub-studies.
  • Comparing approximate imputation models with imputation using rejection sampling for drawing missing values.

Main Results:

  • Multiple imputation (MI) significantly improves efficiency in nested case-control and case-cohort studies, especially when surrogate exposure data is present in the full cohort.
  • The proposed MI method demonstrates superior performance compared to traditional counter-matching in nested case-control studies and weighted analyses in case-cohort studies.
  • Approximate imputation models are effective, but imputation using rejection sampling performs better in the presence of interactions or non-linear terms in the outcome model.

Conclusions:

  • Multiple imputation (MI) offers a powerful strategy to maximize the utility of data in nested case-control and case-cohort studies by integrating information from the entire cohort.
  • This approach leads to substantial efficiency gains, improving the power and precision of exposure-disease association estimates.
  • The choice between approximate imputation and rejection sampling depends on the complexity of the outcome model, with rejection sampling being more robust for non-linear relationships.