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Testing and estimating model-adjusted effect-measure modification using marginal structural models and complex survey

Babette A Brumback1, Erin D Bouldin, Hao W Zheng

  • 1Department of Epidemiology and Biostatistics, University of Florida, Gainesville, 32610-0231, USA. brumback@ufl.edu

American Journal of Epidemiology
|August 31, 2010
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method using marginal structural models (MSMs) to analyze complex survey data, offering an alternative to risk averaging for estimating health risks and effect modification.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Services Research

Background:

  • Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data is crucial for public health research.
  • Existing methods like risk averaging (using SUDAAN) have limitations when analyzing complex survey data.
  • Marginal structural models (MSMs) offer a potential alternative for robust risk estimation in such settings.

Purpose of the Study:

  • To present and evaluate an alternative approach for estimating model-adjusted risks from complex survey data using marginal structural models (MSMs).
  • To compare the MSM approach with the traditional risk-averaging method.
  • To assess effect modification by age on the risk of cost barriers to healthcare among individuals with and without disabilities.

Main Methods:

  • Employed marginal structural models (MSMs) with inverse probability-of-exposure weights, incorporating survey weights for confounding adjustment.
  • Developed and utilized SAS code for MSM parameter estimation and testing effect-measure modification.
  • Re-programmed the risk-averaging approach in SAS for comparative analysis.

Main Results:

  • The study successfully implemented and compared the MSM approach with risk averaging using real-world survey data.
  • SAS code was provided for both the MSM and risk-averaging methods, facilitating their application.
  • The analysis assessed age-related effect modification on the disparity in healthcare cost barriers between disabled and non-disabled populations.

Conclusions:

  • Marginal structural models (MSMs) provide a viable alternative for estimating risks and effect modification from complex survey data.
  • The developed SAS code enables researchers to apply both MSM and risk-averaging methods for comprehensive analysis.
  • The findings contribute to understanding disparities in healthcare access and the role of age as a modifier.