Testing And Estimating Model-Adjusted Effect-Measure Modification Using Marginal Structural Models And Complex Survey Data

ASU Author/Contributor (non-ASU co-authors, if there are any, appear on document)
Erin Bouldin, Assistant Professor, PhD (Creator)
Appalachian State University (ASU )
Web Site: https://library.appstate.edu/

Abstract: Recently, it has been shown how to estimate model-adjusted risks, risk differences, and risk ratios from complex survey data based on risk averaging and SUDAAN (Research Triangle Institute, Research Triangle Park, North Carolina). The authors present an alternative approach based on marginal structural models (MSMs) and SAS (SAS Institute, Inc., Cary, North Carolina). The authors estimate the parameters of the MSM using inverse weights that are the product of 2 terms. The first term is a survey weight that adjusts the sample to represent the unstandardized population. The second term is an inverse-probability-of-exposure weight that standardizes the population in order to adjust for confounding; it must be estimated using the survey weights. The authors show how to use the MSM parameter estimates and contrasts to test and estimate effect-measure modification; SAS code is provided. They also explain how to program the previous risk-averaging approach in SAS. The 2 methods are applied and compared using data from the 2007 Florida Behavioral Risk Factor Surveillance System Survey to assess effect modification by age of the difference in risk of cost barriers to health care between persons with disability and persons without disability.

Additional Information

Babette A. Brumback, Erin D. Bouldin, Hao W. Zheng, Michael B. Cannell, Elena M. Andresen, Testing and Estimating Model-Adjusted Effect-Measure Modification Using Marginal Structural Models and Complex Survey Data, American Journal of Epidemiology, Volume 172, Issue 9, 1 November 2010, Pages 1085–1091, https://doi.org/10.1093/aje/kwq244. Publisher version of record available at: https://academic.oup.com/aje/article/172/9/1085/148053?
Language: English
Date: 2010
health surveys, heterogeneity, interaction, logistic regression, models, statistical, probability weighting, standardization, survey analysis

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