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Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation

Gabriel, Erin E. (author)
Section of Biostatistics, Department of Public Health University of Copenhagen Copenhagen Denmark
Sachs, Michael C. (author)
Section of Biostatistics, Department of Public Health University of Copenhagen Copenhagen Denmark
Martinussen, Torben (author)
Section of Biostatistics, Department of Public Health University of Copenhagen Copenhagen Denmark
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Waernbaum, Ingeborg, 1972- (author)
Uppsala universitet,Statistiska institutionen
Goetghebeur, Els (author)
Department of Applied Mathematics, Computer Science and Statistics Ghent University Ghent Belgium
Vansteelandt, Stijn (author)
Department of Applied Mathematics, Computer Science and Statistics Ghent University Ghent Belgium
Sjölander, Arvid (author)
Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden
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 (creator_code:org_t)
John Wiley & Sons, 2024
2024
English.
In: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 43:3, s. 534-547
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  • Journal article (peer-reviewed)
Abstract Subject headings
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  • There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g-formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains ‘unbalanced’ even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.

Subject headings

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

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Statistics
Statistik

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