Doubly robust adaptive LASSO for effect modifier discovery

Bahamyirou Asma (), Schnitzer Mireille E. (), Kennedy Edward H., Blais Lucie and Yang Yi
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Bahamyirou Asma: Pharmacie, Université de Montréal, 2940, chemin de la Polytechnique, Montreal, QC, H3C 3J7, Canada
Schnitzer Mireille E.: Faculté de pharmacie, Université de Montréal, Pavillon Jean-Coutu, 2940 ch de la Polytechnique, Office #2236, Montreal, QC, Canada
Kennedy Edward H.: Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA, 15213-3815, USA
Blais Lucie: Faculté de pharmacie, Université de Montréal, Montreal, QC, Canada
Yang Yi: Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada

The International Journal of Biostatistics, 2022, vol. 18, issue 2, 307-327

Abstract: Effect modification occurs when the effect of a treatment on an outcome differsaccording to the level of some pre-treatment variable (the effect modifier). Assessing an effect modifier is not a straight-forward task even for a subject matter expert. In this paper, we propose a two-stageprocedure to automatically selecteffect modifying variables in a Marginal Structural Model (MSM) with a single time point exposure based on the two nuisance quantities (the conditionaloutcome expectation and propensity score). We highlight the performance of our proposal in a simulation study. Finally, to illustrate tractability of our proposed methods, we apply them to analyze a set of pregnancy data. We estimate the conditional expected difference in the counterfactual birth weight if all women were exposed to inhaled corticosteroids during pregnancy versus the counterfactual birthweight if all women were not, using data from asthma medications during pregnancy.

Keywords: adaptive LASSO; doubly robust; effect modification; selective inference (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/ijb-2020-0073

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