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Doubly robust testing and estimation of model-adjusted effect-measure modification with complex survey data.

Hao W Zheng1, Babette A Brumback, Xiaomin Lu

  • 1Department of Biostatistics, University of Florida, Gainesville, FL 32611, U.S.A.

Statistics in Medicine
|July 27, 2012
PubMed
Summary

Doubly robust methods improve estimation of population-averaged exposure effects and effect modification in complex survey data. These methods offer robust results even if one model is misspecified, enhancing health disparities research.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Services Research

Background:

  • Model-based standardization adjusts for confounding in population-averaged exposure effect estimation.
  • This methodology extends to estimating and testing effect modification within subgroups.
  • Previous extensions applied to complex survey data, using disability status and healthcare cost barriers as an example.

Purpose of the Study:

  • To develop and apply doubly robust approaches for testing and estimating effect modification with complex survey data.
  • To assess the performance of doubly robust methods compared to traditional exposure and outcome modeling approaches.
  • To develop goodness-of-fit tests for exposure and outcome models using doubly robust strategies.

Main Methods:

  • Developed two doubly robust methods for effect modification analysis in complex survey data.
  • Applied these methods to the 2007 Florida Behavioral Risk Factor Surveillance System Survey (BRFSS) data.
  • Conducted a simulation study to compare doubly robust, exposure modeling, and outcome modeling approaches.

Main Results:

  • Doubly robust approaches provide reliable estimates when either the exposure or outcome model is correctly specified.
  • Contrasting results were observed between exposure modeling and outcome modeling approaches in the BRFSS data.
  • Goodness-of-fit tests for exposure and outcome models were developed and applied using the doubly robust framework.

Conclusions:

  • Doubly robust methods offer a more reliable approach to estimating and testing effect modification in complex survey data.
  • These methods enhance the robustness of findings when model assumptions may be violated.
  • The developed techniques provide valuable tools for analyzing health disparities and effect modification in complex survey datasets.