AI Summary & Key Takeaways
AI InsightThe stratification-matching methodshould be preferred for not only low standardized bias and high bias reduction, but also highaccuracy rate.
📌 3 Key Takeaways
Abstract
There has been a growing interest in using observational studies to estimate treatment effectson outcomes where treatment selection is often influenced by covariates. Recently, propensityscore matching (PSM) method has increasingly being used to reduce bias in estimatedtreatment effect for observational studies. Greedy Matching (GM), one of the PSM methods,is widely preferred in many studies because of the calculation simplicity of the method.However, GM is still open to be evaluated in terms of bias reduction and classificationperformances. For this purpose, data including cigarette usage of 17242 individuals in Turkeywere used for the comparison of nearest neighbor, caliper, stratification, Mahalanobis metric,and combined propensity score and Mahalanobis metric matching methods in terms of averagestandardized bias, bias reduction, and accuracy rate. The stratification-matching methodshould be preferred for not only low standardized bias and high bias reduction, but also highaccuracy rate.