Statistical criteria for selecting the optimal number of untreated subjects matched to each treated subject when using many-to-one matching on the propensity score.
Propensity-score matching is increasingly being used to estimate the effects of treatments using observational data. In many-to-one (M:1) matching on the propensity score, M untreated subjects are matched to each treated subject using the propensity score. The authors used Monte Carlo simulations to examine the effect of the choice of M on the statistical performance of matched estimators. They considered matching 1-5 untreated subjects to each treated subject using both nearest-neighbor matching and caliper matching in 96 different scenarios. Increasing the number of untreated subjects matched to each treated subject tended to increase the bias in the estimated treatment effect; conversely, increasing the number of untreated subjects matched to each treated subject decreased the sampling variability of the estimated treatment effect. Using nearest-neighbor matching, the mean squared error of the estimated treatment effect was minimized in 67.7% of the scenarios when 1:1 matching was used. Using nearest-neighbor matching or caliper matching, the mean squared error was minimized in approximately 84% of the scenarios when, at most, 2 untreated subjects were matched to each treated subject. The authors recommend that, in most settings, researchers match either 1 or 2 untreated subjects to each treated subject when using propensity-score matching.
Austin
Peter C
PC
Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. peter.austin@ices.on.ca
D000465:Algorithms; D015982:Bias; D035843:Biomedical Research / Q000706:statistics & numerical data; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D016015:Logistic Models; D016555:Matched-Pair Analysis; D009010:Monte Carlo Method; D018579:Patient Selection; D057216:Propensity Score
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