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Doubly robust methods for handling confounding by cluster.

Johan Zetterqvist1, Stijn Vansteelandt2, Yudi Pawitan3

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Solna, Sweden johan.zetterqvist@ki.se.

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Summary
This summary is machine-generated.

This study introduces a new statistical method, the doubly robust conditional generalized estimating equation (DRCGEE) estimator, to improve the accuracy of exposure-outcome association studies in family settings. The DRCGEE method reduces bias caused by model misspecification, offering more reliable results in clustered data analysis.

Keywords:
Conditional estimating equationsDoubly robust estimationEpidemiologyG-estimationObservational studies

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Genetics

Background:

  • Clustered study designs, like family studies, face confounding from both cluster-constant and cluster-varying factors.
  • While within-cluster analysis addresses constant confounders, cluster-varying confounders require regression modeling, which risks bias if the model is misspecified.

Purpose of the Study:

  • To develop a statistical method that is less sensitive to model misspecification when estimating exposure-outcome associations in clustered data.
  • To provide a more robust estimation technique for within-cluster associations, offering improved reliability in epidemiological and genetic studies.

Main Methods:

  • Proposed an augmentation of the standard outcome model with an auxiliary exposure model.
  • Derived a doubly robust conditional generalized estimating equation (DRCGEE) estimator.
  • Implemented the DRCGEE estimator in an R package for practical application.

Main Results:

  • The DRCGEE estimator is consistent if either the outcome model or the exposure model is correctly specified, not necessarily both.
  • This dual robustness increases the likelihood of valid inference on within-cluster associations.
  • Applied the method to analyze the association between prenatal smoking and offspring cognitive abilities in sibling data.

Conclusions:

  • The DRCGEE estimator offers a more reliable approach to estimating within-cluster exposure-outcome associations compared to standard methods.
  • Researchers gain increased confidence in their findings due to the reduced sensitivity to model misspecification.
  • The developed R package facilitates the application of this advanced statistical technique in relevant research areas.