期刊
STATISTICS IN MEDICINE
卷 32, 期 8, 页码 1383-1393出版社
WILEY
DOI: 10.1002/sim.5599
关键词
bias; causal inference; marginal structural model; regression analysis; model specification
类别
资金
- Fonds de la Recherche en Sante du Quebec (FRSQ)
- FRSQ
- Canadian Network for Observational Drug Effect Studies (CNODES)
- NIH [R01-AA-01759, AG027400]
- NIH/NICHD [R00-HD-06-3961]
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- Thrasher Research Fund
- National Health Research and Development Program (Health Canada)
- United Nations Children's Fund
- European Regional Office of the World Health Organization
- National Institute of Allergy and Infectious Diseases [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 [UO1-HD-32632]
- National Cancer Institute, the National Institute on Drug Abuse
- National Institute on Deafness and Other Communication Disorders
- National Institute of Allergy and Infectious Diseases
- National Cancer Institute [UO1-AI-35042, UL1-RR025005 (GCRC), UO1-AI-35043, UO1-AI-35039, UO1-AI-35040, UO1-AI-35041]
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. Copyright (c) 2012 John Wiley & Sons, Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据