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

Babette A. Brumback, Erin D. Bouldin, Hao W. Zheng, Michael B. Cannell, Elena Andresen

Research output: Contribution to journalArticle

16 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1085-1091
Number of pages7
JournalAmerican Journal of Epidemiology
Volume172
Issue number9
DOIs
StatePublished - Nov 1 2010
Externally publishedYes

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Structural Models
Weights and Measures
Behavioral Risk Factor Surveillance System
Disabled Persons
Population
Odds Ratio
Surveys and Questionnaires
Delivery of Health Care
Costs and Cost Analysis
Research

Keywords

  • health surveys
  • heterogeneity
  • interaction
  • logistic regression
  • models statistical
  • probability weighting
  • standardization
  • survey analysis

ASJC Scopus subject areas

  • Epidemiology

Cite this

Testing and estimating model-adjusted effect-measure modification using marginal structural models and complex survey data. / Brumback, Babette A.; Bouldin, Erin D.; Zheng, Hao W.; Cannell, Michael B.; Andresen, Elena.

In: American Journal of Epidemiology, Vol. 172, No. 9, 01.11.2010, p. 1085-1091.

Research output: Contribution to journalArticle

Brumback, Babette A. ; Bouldin, Erin D. ; Zheng, Hao W. ; Cannell, Michael B. ; Andresen, Elena. / Testing and estimating model-adjusted effect-measure modification using marginal structural models and complex survey data. In: American Journal of Epidemiology. 2010 ; Vol. 172, No. 9. pp. 1085-1091.
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