title: Optimal full matching for survival outcomes: a method that merits more widespread use.
abstract: Matching on the propensity score is a commonly used analytic method for estimating the effects of treatments on outcomes. Commonly used propensity score matching methods include nearest neighbor matching and nearest neighbor caliper matching. Rosenbaum (1991) proposed an optimal full matching approach, in which matched strata are formed consisting of either one treated subject and at least one control subject or one control subject and at least one treated subject. Full matching has been used rarely in the applied literature. Furthermore, its performance for use with survival outcomes has not been rigorously evaluated. We propose a method to use full matching to estimate the effect of treatment on the hazard of the occurrence of the outcome. An extensive set of Monte Carlo simulations were conducted to examine the performance of optimal full matching with survival analysis. Its performance was compared with that of nearest neighbor matching, nearest neighbor caliper matching, and inverse probability of treatment weighting using the propensity score. Full matching has superior performance compared with that of the two other matching algorithms and had comparable performance with that of inverse probability of treatment weighting using the propensity score. We illustrate the application of full matching with survival outcomes to estimate the effect of statin prescribing at hospital discharge on the hazard of post-discharge mortality in a large cohort of patients who were discharged from hospital with a diagnosis of acute myocardial infarction. Optimal full matching merits more widespread adoption in medical and epidemiological research. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
authors.affiliation: Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.|Institute of Health Management, Policy and Evaluation, University of Toronto, Toronto, Ontario, Canada.|Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada.
authors.forename: Peter C
authors.initials: PC
authors.lastname: Austin
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.|Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.|Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Stuart
mesh_terms: D056808:Biostatistics / Q000379:methods*; D016022:Case-Control Studies; D003198:Computer Simulation; D016001:Confidence Intervals; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009010:Monte Carlo Method; D009203:Myocardial Infarction / Q000401:mortality / Q000628:therapy; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D057216:Propensity Score; D016016:Proportional Hazards Models; D012044:Regression Analysis; D016019:Survival Analysis
keywords: Monte Carlo simulations; bias; full matching; matching; observational studies; optimal matching; propensity score