Journal of Data Science

The paper presents an investigation of estimating treatment effect using differ- ent matching methods through Monte Carlo simulation. The study proposed a new method which is computationally efficient and convenient in implication—largest caliper matching and compared the performance with other five popular matching methods. The bias, empirical standard deviation and the mean square error of the estimates in the simulation are checked under different treatment prevalence and different distributions of covariates. It is shown that largest caliper matching improves estimation of the population treatment effect in a wide range of settings compare to other methods. It reduces the bias if the data contains the selection on observables and treatment imbalances. Also, findings about the relative performance of the different matching methods are provided to help practitioners determine which method should be used under certain situations. An application of these methods is implemented on the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT) data and, important demographic and socioeconomic factors that may affect the clinical outcome are also reported in this paper.