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Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies.
Pharm Stat. 2011 Mar-Apr; 10(2):150-61.PS

Abstract

In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences in means (for continuous outcomes) and risk differences (for binary outcomes). When estimating differences in means or risk differences, we recommend that researchers match on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score. When at least some of the covariates were continuous, then either this value, or one close to it, minimized the mean square error of the resultant estimated treatment effect. It also eliminated at least 98% of the bias in the crude estimator, and it resulted in confidence intervals with approximately the correct coverage rates. Furthermore, the empirical type I error rate was approximately correct. When all of the covariates were binary, then the choice of caliper width had a much smaller impact on the performance of estimation of risk differences and differences in means.

Authors+Show Affiliations

Institute for Clinical Evaluative Sciences, Toronto, Ont., Canada. peter.austin@ices.on.ca

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

20925139

Citation

Austin, Peter C.. "Optimal Caliper Widths for Propensity-score Matching when Estimating Differences in Means and Differences in Proportions in Observational Studies." Pharmaceutical Statistics, vol. 10, no. 2, 2011, pp. 150-61.
Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10(2):150-61.
Austin, P. C. (2011). Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceutical Statistics, 10(2), 150-61. https://doi.org/10.1002/pst.433
Austin PC. Optimal Caliper Widths for Propensity-score Matching when Estimating Differences in Means and Differences in Proportions in Observational Studies. Pharm Stat. 2011 Mar-Apr;10(2):150-61. PubMed PMID: 20925139.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. A1 - Austin,Peter C, PY - 2010/10/7/entrez PY - 2010/10/7/pubmed PY - 2012/6/12/medline SP - 150 EP - 61 JF - Pharmaceutical statistics JO - Pharm Stat VL - 10 IS - 2 N2 - In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences in means (for continuous outcomes) and risk differences (for binary outcomes). When estimating differences in means or risk differences, we recommend that researchers match on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score. When at least some of the covariates were continuous, then either this value, or one close to it, minimized the mean square error of the resultant estimated treatment effect. It also eliminated at least 98% of the bias in the crude estimator, and it resulted in confidence intervals with approximately the correct coverage rates. Furthermore, the empirical type I error rate was approximately correct. When all of the covariates were binary, then the choice of caliper width had a much smaller impact on the performance of estimation of risk differences and differences in means. SN - 1539-1612 UR - https://www.unboundmedicine.com/medline/citation/20925139/Optimal_caliper_widths_for_propensity_score_matching_when_estimating_differences_in_means_and_differences_in_proportions_in_observational_studies_ L2 - https://doi.org/10.1002/pst.433 DB - PRIME DP - Unbound Medicine ER -