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Use of multiple imputation in supersampled nested case‐control and case‐cohort studies

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  • Ørnulf Borgan
  • Ruth H. Keogh
  • Aleksander Njøs

Abstract

Nested case‐control and case‐cohort studies are useful for studying associations between covariates and time‐to‐event when some covariates are expensive to measure. Full covariate information is collected in the nested case‐control or case‐cohort sample only, while cheaply measured covariates are often observed for the full cohort. Standard analysis of such case‐control samples ignores any full cohort data. Previous work has shown how data for the full cohort can be used efficiently by multiple imputation of the expensive covariate(s), followed by a full‐cohort analysis. For large cohorts this is computationally expensive or even infeasible. An alternative is to supplement the case‐control samples with additional controls on which cheaply measured covariates are observed. We show how multiple imputation can be used for analysis of such supersampled data. Simulations show that this brings efficiency gains relative to a traditional analysis and that the efficiency loss relative to using the full cohort data is not substantial.

Suggested Citation

  • Ørnulf Borgan & Ruth H. Keogh & Aleksander Njøs, 2023. "Use of multiple imputation in supersampled nested case‐control and case‐cohort studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 13-37, March.
    • Handle: RePEc:bla:scjsta:v:50:y:2023:i:1:p:13-37
    DOI: 10.1111/sjos.12624
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    References listed on IDEAS

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    1. Thomas H. Scheike & Torben Martinussen, 2004. "Maximum Likelihood Estimation for Cox's Regression Model Under Case–Cohort Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 283-293, June.
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