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. 2015 Dec 30;34(30):3949-67.
doi: 10.1002/sim.6602. Epub 2015 Aug 6.

Optimal full matching for survival outcomes: a method that merits more widespread use

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

Optimal full matching for survival outcomes: a method that merits more widespread use

Peter C Austin et al. Stat Med. .

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.

Keywords: Monte Carlo simulations; bias; full matching; matching; observational studies; optimal matching; propensity score.

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Figures

Figure 1
Figure 1
Standardized differences for the four confounding variables (moderate confounding). NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting.
Figure 2
Figure 2
Standardized differences for the four confounding variables (strong confounding). NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting.
Figure 3
Figure 3
Mean estimated hazard ratio across simulated datasets. NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting; ATT, average effect of treatment in the treated.
Figure 4
Figure 4
Standard deviation (SD) of estimated hazard ratios across simulated datasets. NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting; ATT, average effect of treatment in the treated.
Figure 5
Figure 5
Ratio of mean estimated standard error to empirical standard deviation of the estimated log‐hazard ratio. NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting; ATT, average effect of treatment in the treated.
Figure 6
Figure 6
Mean squared error (MSE) of estimated hazard ratio across simulated datasets. NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting; ATT, average effect of treatment in the treated.
Figure 7
Figure 7
Empirical coverage rates of 95% confidence intervals (CIs). NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting; ATT, average effect of treatment in the treated.
Figure 8
Figure 8
Empirical type I error rates. NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting; ATT, average effect of treatment in the treated.
Figure 9
Figure 9
Mixed covariates: standardized differences for the four confounding variables. NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting.
Figure 10
Figure 10
Mixed covariates: mean and standard deviation of estimated hazard ratios across simulated datasets. NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting; ATT, average effect of treatment in the treated.
Figure 11
Figure 11
Mixed covariates: empirical versus estimated standard error and mean squared error (MSE). NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting; ATT, average effect of treatment in the treated.
Figure 12
Figure 12
Mixed covariates: empirical coverage rates of 95% confidence intervals (CIs) and empirical type I error rates. NNM, nearest neighbor matching; IPTW, inverse probability of treatment weighting; ATT, average effect of treatment in the treated.
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