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Doubly robust conditional logistic regression.

Johan Zetterqvist1, Karel Vermeulen2, Stijn Vansteelandt3,4

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|August 3, 2019
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Summary
This summary is machine-generated.

This study introduces a new doubly robust estimator for odds ratios in clustered data, improving accuracy in epidemiologic research. The method reduces bias from model misspecification in conditional logistic regression, enhancing reliability for exposure-outcome associations.

Keywords:
conditional logistic regressionconditional maximum likelihooddoubly robust estimation

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical modeling

Background:

  • Epidemiologic studies often assess binary exposure-outcome associations using conditional logistic regression, especially with clustered data like matched case-control or co-twin studies.
  • Cluster-constant covariates are absorbed into intercepts, while cluster-varying covariates are explicitly modeled.

Purpose of the Study:

  • To propose a novel doubly robust estimator for the exposure-outcome odds ratio in conditional logistic regression models.
  • To mitigate bias arising from misspecification of cluster-varying covariates in the model.

Main Methods:

  • Developed a doubly robust estimator utilizing two conditional logistic regression models: one prospective and one retrospective.
  • The estimator is consistent if at least one of the two models is correctly specified.

Main Results:

  • The proposed doubly robust estimator demonstrates robustness against misspecification of cluster-varying covariates.
  • Simulations and re-analysis of a matched case-control study on induced abortion and infertility validated the method's properties.

Conclusions:

  • The doubly robust estimator offers improved accuracy and reliability for estimating odds ratios in clustered epidemiologic data.
  • This method enhances the analysis of exposure-outcome associations when dealing with complex data structures and potential model misspecification.