Premium
An information criterion for marginal structural models
Author(s) -
Platt Robert W.,
Brookhart M. Alan,
Cole Stephen R.,
Westreich Daniel,
Schisterman Enrique F.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5599
Subject(s) - marginal structural model , marginal model , econometrics , specification , inference , computer science , metric (unit) , observational study , parametric statistics , causal inference , regression , semiparametric regression , marginal likelihood , regression analysis , statistics , mathematics , machine learning , bayesian probability , artificial intelligence , economics , operations management
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.