title: Comparing the performance of propensity score methods in healthcare database studies with rare outcomes.
abstract: Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of events observed in a small number of patients. In addition, studies constructed from electronic healthcare databases, for example, administrative claims data, often adjust for many, possibly hundreds, of potential confounders. Despite the importance of maximizing efficiency when there are many confounders and few observed outcome events, there has been relatively little research on the relative performance of different propensity score methods in this context. In this paper, we compare a wide variety of propensity-based estimators of the marginal relative risk. In contrast to prior research that has focused on specific statistical methods in isolation of other analytic choices, we instead consider a method to be defined by the complete multistep process from propensity score modeling to final treatment effect estimation. Propensity score model estimation methods considered include ordinary logistic regression, Bayesian logistic regression, lasso, and boosted regression trees. Methods for utilizing the propensity score include pair matching, full matching, decile strata, fine strata, regression adjustment using one or two nonlinear splines, inverse propensity weighting, and matching weights. We evaluate methods via a 'plasmode' simulation study, which creates simulated datasets on the basis of a real cohort study of two treatments constructed from administrative claims data. Our results suggest that regression adjustment and matching weights, regardless of the propensity score model estimation method, provide lower bias and mean squared error in the context of rare binary outcomes. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A.
authors.forename: Jessica M
authors.identifier: 0000-0002-8941-4116
authors.initials: JM
authors.lastname: Franklin
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A.
authors.forename: Wesley
authors.initials: W
authors.lastname: Eddings
authors.affiliation: Institute for Clinical Evaluative Sciences, Toronto, Canada.
authors.forename: Peter C
authors.identifier: 0000-0003-3337-233X
authors.initials: PC
authors.lastname: Austin
authors.affiliation: Departments of Mental Health, Biostatistics, and Healthcare Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, U.S.A.
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Stuart
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schneeweiss
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D015984:Causality; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D011336:Probability; D057216:Propensity Score; D012044:Regression Analysis; D016896:Treatment Outcome
keywords: epidemiology; healthcare databases; propensity score; risk ratio; simulation