An information criterion for marginal structural models

Robert W. Platt, M. Alan Brookhart, Stephen R. Cole, Daniel Westreich, Enrique F. Schisterman

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Marginal structural models were developed as a semiparametric alternative to the G-computation formula to estimate causal effects of exposures. In practice, these models are often specified using parametric regression models. As such, the usual conventions regarding regression model specification apply. This paper outlines strategies for marginal structural model specification and considerations for the functional form of the exposure metric in the final structural model. We propose a quasi-likelihood information criterion adapted from use in generalized estimating equations. We evaluate the properties of our proposed information criterion using a limited simulation study. We illustrate our approach using two empirical examples. In the first example, we use data from a randomized breastfeeding promotion trial to estimate the effect of breastfeeding duration on infant weight at 1year. In the second example, we use data from two prospective cohorts studies to estimate the effect of highly active antiretroviral therapy on CD4 count in an observational cohort of HIV-infected men and women. The marginal structural model specified should reflect the scientific question being addressed but can also assist in exploration of other plausible and closely related questions. In marginal structural models, as in any regression setting, correct inference depends on correct model specification. Our proposed information criterion provides a formal method for comparing model fit for different specifications.

Original languageEnglish (US)
Pages (from-to)1383-1393
Number of pages11
JournalStatistics in Medicine
Volume32
Issue number8
DOIs
StatePublished - Apr 15 2013
Externally publishedYes

Keywords

  • Bias
  • Causal inference
  • Marginal structural model
  • Model specification
  • Regression analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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