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A Marginal Structural Modeling Approach with Super Learning for a Study on Oral Bisphosphonate Therapy and Atrial Fibrillation

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  • Neugebauer Romain
  • Chandra Malini

    (Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA)

  • Paredes Antonio

    (Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Springs, MD, USA)

  • J. Graham David
  • McCloskey Carolyn

    (Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Springs, MD, USA)

  • S. Go Alan

    (Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA; Departments of Epidemiology, Biostatistics and Medicine, University of California, San Francisco, CA, USA; Department of Health Reserach and Policy, Stanford University, Palo Alto, CA, USA)

Abstract

Purpose: Observational studies designed to investigate the safety of a drug in a postmarketing setting typically aim to examine rare and non-acute adverse effects in a population that is not restricted to particular patient subgroups for which the therapy, typically a drug, was originally approved. Large healthcare databases and, in particular, rich electronic medical record (EMR) databases, are well suited for the conduct of these safety studies since they can provide detailed longitudinal information on drug exposure, confounders, and outcomes for large and representative samples of patients that are considered for treatment in clinical settings. Analytic efforts for drawing valid causal inferences in such studies are faced with three challenges: (1) the formal definition of relevant effect measures addressing the safety question of interest; (2) the development of analytic protocols to estimate such effects based on causal methodologies that can properly address the problems of time-dependent confounding and selection bias due to informative censoring, and (3) the practical implementation of such protocols in a large clinical/medical database setting. In this article, we describe an effort to specifically address these challenges with marginal structural modeling based on inverse probability weighting with data reduction and super learning.

Suggested Citation

  • Neugebauer Romain & Chandra Malini & Paredes Antonio & J. Graham David & McCloskey Carolyn & S. Go Alan, 2013. "A Marginal Structural Modeling Approach with Super Learning for a Study on Oral Bisphosphonate Therapy and Atrial Fibrillation," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 21-50, June.
    • Handle: RePEc:bpj:causin:v:1:y:2013:i:1:p:21-50:n:2
    DOI: 10.1515/jci-2012-0003
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