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A note on G-estimation of causal risk ratios

Oliver Dukes (UGent) and Stijn Vansteelandt (UGent)
(2018) AMERICAN JOURNAL OF EPIDEMIOLOGY. 187(5). p.1079-1084
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Abstract
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.
Keywords
causal inference, G-estimation, propensity scores, risk ratios, FAILURE-TIME-MODELS, STRUCTURAL NESTED MODELS, PROPENSITY SCORE, VARYING TREATMENTS, INFERENCE, MORTALITY, EXPOSURE, COHORT, NONCOMPLIANCE, PROMISE

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Citation

Please use this url to cite or link to this publication:

Chicago
Dukes, Oliver, and Stijn Vansteelandt. 2018. “A Note on G-estimation of Causal Risk Ratios.” American Journal of Epidemiology 187 (5): 1079–1084.
APA
Dukes, O., & Vansteelandt, S. (2018). A note on G-estimation of causal risk ratios. AMERICAN JOURNAL OF EPIDEMIOLOGY, 187(5), 1079–1084.
Vancouver
1.
Dukes O, Vansteelandt S. A note on G-estimation of causal risk ratios. AMERICAN JOURNAL OF EPIDEMIOLOGY. 2018;187(5):1079–84.
MLA
Dukes, Oliver, and Stijn Vansteelandt. “A Note on G-estimation of Causal Risk Ratios.” AMERICAN JOURNAL OF EPIDEMIOLOGY 187.5 (2018): 1079–1084. Print.
@article{8570423,
  abstract     = {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.},
  author       = {Dukes, Oliver and Vansteelandt, Stijn},
  issn         = {0002-9262},
  journal      = {AMERICAN JOURNAL OF EPIDEMIOLOGY},
  keyword      = {causal inference,G-estimation,propensity scores,risk ratios,FAILURE-TIME-MODELS,STRUCTURAL NESTED MODELS,PROPENSITY SCORE,VARYING TREATMENTS,INFERENCE,MORTALITY,EXPOSURE,COHORT,NONCOMPLIANCE,PROMISE},
  language     = {eng},
  number       = {5},
  pages        = {1079--1084},
  title        = {A note on G-estimation of causal risk ratios},
  url          = {http://dx.doi.org/10.1093/aje/kwx347},
  volume       = {187},
  year         = {2018},
}

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