Download or read book The Performance of Propensity Score Methods to Estimate the Average Treatment Effect in Observational Studies with Selection Bias written by Sungur Gurel and published by . This book was released on 2012 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: We investigated the performance of four different propensity score (PS) methods to reduce selection bias in estimates of the average treatment effect (ATE) in observational studies: inverse probability of treatment weighting (IPTW), truncated inverse probability of treatment weighting (TIPTW), optimal full propensity score matching (OFPSM), and propensity score stratification (PSS). We compared these methods in combination with three methods of standard error estimation: weighted least squares regression (WLS), Taylor series linearization (TSL), and jackknife (JK). We conducted a Monte Carlo Simulation study manipulating the number of subjects and the ratio of treated to total sample size. The results indicated that IPTW and OFPSM methods removed almost all of the bias while TIPTW and PSS removed about 90% of the bias. Some of TSL and JK standard errors were acceptable, some marginally overestimated, and some moderately overestimated. For the lower ratio of treated on sample sizes, all of the WLS standard errors were strongly underestimated, as designs get balanced, the underestimation gets less serious. Especially for the OFPSM, all of the TSL and JK standard errors were overestimated and WLS standard errors under estimated under all simulated conditions.