Supporting information may be found in the online version of this article.
An information criterion for marginal structural models†
Article first published online: 12 SEP 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 32, Issue 8, pages 1383–1393, 15 April 2013
How to Cite
Platt, R. W., Brookhart, M. A., Cole, S. R., Westreich, D. and Schisterman, E. F. (2013), An information criterion for marginal structural models. Statist. Med., 32: 1383–1393. doi: 10.1002/sim.5599
- Issue published online: 14 MAR 2013
- Article first published online: 12 SEP 2012
- Manuscript Accepted: 9 AUG 2012
- Manuscript Received: 28 JUN 2011
- NIH. Grant Number: R01-AA-01759
- NIH. Grant Number: AG027400
- NIH/NICHD. Grant Number: R00-HD-06-3961
- National Institute of Allergy and Infectious Diseases. Grant Numbers: UO1-AI-35004, UO1-AI-31834, UO1-AI-34994, UO1-AI-34989, UO1-AI-34993, UO1-AI-42590
- National Institute of Child Health and Human Development. Grant Number: UO1-HD-32632
- National Cancer Institute. Grant Numbers: UO1-AI-35042, UL1-RR025005 (GCRC), UO1-AI-35043, UO1-AI-35039, UO1-AI-35040, UO1-AI-35041
- causal inference;
- marginal structural model;
- regression analysis;
- model specification
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 1 year. 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. Copyright © 2012 John Wiley & Sons, Ltd.