title: Evaluating possible confounding by prescriber in comparative effectiveness research.
abstract: In nonrandomized studies of comparative effectiveness of medications, the prescriber may be the most important determinant of treatment assignment, yet the majority of analyses ignore the prescriber. Via Monte Carlo simulation, we evaluated the bias of 3 approaches that utilize the prescriber in analysis compared against the default approach that ignores the prescriber. Prescriber preference instrumental variable (IV) analyses were unbiased when IV criteria were met, which required no clustering of unmeasured patient characteristics within prescriber. In all other scenarios, IV analyses were highly biased, and stratification on the prescriber reduced confounding bias at the patient or prescriber levels. Including a prescriber random intercept in the propensity score model reversed the direction of confounding from measured patient factors and resulted in unpredictable changes in bias. Therefore, we recommend caution when using the IV approach, particularly when the instrument is weak. Stratification on the prescriber may be more robust; this approach warrants additional research.
authors: From the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
authors: Jessica M
authors: JM
authors: Franklin
authors: Sebastian
authors: S
authors: Schneeweiss
authors: Krista F
authors: KF
authors: Huybrechts
authors: Robert J
authors: RJ
authors: Glynn
mesh_terms: D015982:Bias; D057186:Comparative Effectiveness Research / Q000379:methods*; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D010818:Practice Patterns, Physicians'; D057216:Propensity Score
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