Journal
AMERICAN JOURNAL OF EPIDEMIOLOGY
Volume 187, Issue 5, Pages 1079-1084Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwx347
Keywords
causal inference; G-estimation; propensity scores; risk ratios
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Funding
- Research Foundation Flanders [1S05916N]
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G-estimation is a flexible, semiparametric approach for estimating exposure effects in epidemiologic studies. It has several underappreciated advantages over other propensity score-based methods popular in epidemiology, which we review in this article. However, it is rarely used in practice, due to a lack of off-the-shelf software. To rectify this, we show a simple trick for obtaining G-estimators of causal risk ratios using existing generalized estimating equations software. We extend the procedure to more complex settings with time-varying confounders.
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