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Efficient estimation of indirect effects in case-control studies using a unified likelihood framework.

Glen A Satten1, Sarah W Curtis2, Claudia Solis-Lemus3

  • 1Department of Gynecology and Obstetrics, Emory University, Atlanta, Georgia, USA.

Statistics in Medicine
|March 30, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel likelihood framework for mediation analysis in case-control studies. This method improves the efficiency of indirect effect estimates by utilizing more data features, including the exposure-mediator relationship.

Keywords:
case-control studygenetic epidemiologymediation analysis

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical modeling

Background:

  • Mediation models examine intermediate variables influencing exposure-outcome relationships.
  • Counterfactual frameworks are common for mediation analysis in case-control studies with dichotomous outcomes.
  • Existing methods may not fully leverage all available data, potentially limiting efficiency.

Purpose of the Study:

  • To develop a unified likelihood framework for mediation analysis in case-control studies.
  • To enhance the efficiency of indirect effect estimates by incorporating the exposure-mediator relationship.
  • To provide a flexible approach that accommodates confounders and interactions without requiring disease prevalence.

Main Methods:

  • Developed a unified likelihood framework for mediation analysis in case-control studies.
  • Embedded the counterfactual approach within this new likelihood framework.
  • Incorporated cases within the exposure-mediator model for improved efficiency.
  • Modeled confounders and exposure-mediator interactions.

Main Results:

  • The proposed likelihood approach improves the efficiency of indirect effect estimates compared to standard methods.
  • The framework naturally incorporates cases into the exposure-mediator model.
  • The method does not require knowledge of disease prevalence.
  • Demonstrated utility with simulated and real case-control genetic data for lung cancer.

Conclusions:

  • A unified likelihood framework offers a more efficient approach to mediation analysis in case-control studies.
  • This method enhances understanding of exposure-mediator-outcome pathways.
  • The approach is practical, implementable in standard software, and applicable to various case-control research settings.