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Doubly robust adaptive LASSO for effect modifier discovery

Author

Listed:
  • 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)

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.

Suggested Citation

  • Bahamyirou Asma & Schnitzer Mireille E. & Kennedy Edward H. & Blais Lucie & Yang Yi, 2022. "Doubly robust adaptive LASSO for effect modifier discovery," The International Journal of Biostatistics, De Gruyter, vol. 18(2), pages 307-327, November.
    • Handle: RePEc:bpj:ijbist:v:18:y:2022:i:2:p:307-327:n:5
    DOI: 10.1515/ijb-2020-0073
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