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Estimating the effect of treatment on binary outcomes using full matching on the propensity score.
Stat Methods Med Res. 2017 Dec; 26(6):2505-2525.SM

Abstract

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.

Authors+Show Affiliations

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.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.

Pub Type(s)

Comparative Study
Journal Article

Language

eng

PubMed ID

26329750

Citation

Austin, Peter C., and Elizabeth A. Stuart. "Estimating the Effect of Treatment On Binary Outcomes Using Full Matching On the Propensity Score." Statistical Methods in Medical Research, vol. 26, no. 6, 2017, pp. 2505-2525.
Austin PC, Stuart EA. Estimating the effect of treatment on binary outcomes using full matching on the propensity score. Stat Methods Med Res. 2017;26(6):2505-2525.
Austin, P. C., & Stuart, E. A. (2017). Estimating the effect of treatment on binary outcomes using full matching on the propensity score. Statistical Methods in Medical Research, 26(6), 2505-2525. https://doi.org/10.1177/0962280215601134
Austin PC, Stuart EA. Estimating the Effect of Treatment On Binary Outcomes Using Full Matching On the Propensity Score. Stat Methods Med Res. 2017;26(6):2505-2525. PubMed PMID: 26329750.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Estimating the effect of treatment on binary outcomes using full matching on the propensity score. AU - Austin,Peter C, AU - Stuart,Elizabeth A, Y1 - 2015/09/01/ PY - 2015/9/4/pubmed PY - 2018/8/28/medline PY - 2015/9/3/entrez KW - Monte Carlo simulations KW - Propensity score KW - bias KW - full matching KW - inverse probability of treatment weighting KW - matching KW - observational studies SP - 2505 EP - 2525 JF - Statistical methods in medical research JO - Stat Methods Med Res VL - 26 IS - 6 N2 - 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. SN - 1477-0334 UR - https://www.unboundmedicine.com/medline/citation/26329750/Estimating_the_effect_of_treatment_on_binary_outcomes_using_full_matching_on_the_propensity_score_ L2 - http://journals.sagepub.com/doi/full/10.1177/0962280215601134?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -