Estimating the effect of treatment on binary outcomes using full matching on the propensity score.
Many non-experimental studies use propensity-score methods to estimate causal effects by balancing treatment and control groups on a set of observed baseline covariates. Full matching on the propensity score has emerged as a particularly effective and flexible method for utilizing all available data, and creating well-balanced treatment and comparison groups. However, full matching has been used infrequently with binary outcomes, and relatively little work has investigated the performance of full matching when estimating effects on binary outcomes. This paper describes methods that can be used for estimating the effect of treatment on binary outcomes when using full matching. It then used Monte Carlo simulations to evaluate the performance of these methods based on full matching (with and without a caliper), and compared their performance with that of nearest neighbour matching (with and without a caliper) and inverse probability of treatment weighting. The simulations varied the prevalence of the treatment and the strength of association between the covariates and treatment assignment. Results indicated that all of the approaches work well when the strength of confounding is relatively weak. With stronger confounding, the relative performance of the methods varies, with nearest neighbour matching with a caliper showing consistently good performance across a wide range of settings. We illustrate the approaches using a study estimating the effect of inpatient smoking cessation counselling on survival following hospitalization for a heart attack.
1 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.|2 Institute of Health Management, Policy and Evaluation, University of Toronto, Ontario, Canada.|3 Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Canada.
Peter C
PC
Austin
4 Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.|5 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.|6 Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Elizabeth A
EA
Stuart
D015982:Bias; D056808:Biostatistics / Q000379:methods; D015984:Causality; D003198:Computer Simulation; D016001:Confidence Intervals; D006801:Humans; D009010:Monte Carlo Method; D064887:Observational Studies as Topic / Q000706:statistics & numerical data*; D016017:Odds Ratio; D057216:Propensity Score; D012306:Risk
Monte Carlo simulations; Propensity score; bias; full matching; inverse probability of treatment weighting; matching; observational studies