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

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.