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Comparative Study
. 2017 Dec;26(6):2505-2525.
doi: 10.1177/0962280215601134. Epub 2015 Sep 1.

Estimating the effect of treatment on binary outcomes using full matching on the propensity score

Peter C Austin  1   2   3 , Elizabeth A Stuart  4   5   6
Affiliations
Comparative Study

Estimating the effect of treatment on binary outcomes using full matching on the propensity score

Peter C Austin et al. Stat Methods Med Res. 2017 Dec.

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.

Keywords: Monte Carlo simulations; Propensity score; bias; full matching; inverse probability of treatment weighting; matching; observational studies.

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Figures

Figure 1.
Figure 1.
Mean standardized differences for the 10 baseline variables in original sample.
Figure 2.
Figure 2.
Relative bias in estimating the risk difference.
Figure 3.
Figure 3.
Relative bias in estimating the relative risk.
Figure 4.
Figure 4.
Relative bias in estimating the odds ratio.
Figure 5.
Figure 5.
Mean squared error (MSE) of estimated risk difference.
Figure 6.
Figure 6.
Mean squared error (MSE) of estimated relative risk.
Figure 7.
Figure 7.
Mean squared error (MSE) of estimated odds ratio.
Figure 8.
Figure 8.
Full matching and conditional logistic regression.
Figure 9.
Figure 9.
Full matching with calipers and conditional logistic regression.
See this image and copyright information in PMC

References

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    1. Rubin DB. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Stat Med 2007; 26: 20–36. - PubMed
    1. Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat Med 2008; 27: 2037–2049. - PubMed

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