摘要：The estimation of population/subpopulation average ?treatment effects has been a subject of extensive research. Randomized ?experiments are the ideal way to estimate the effects of ?interventions. However, in many applications, randomized experiments ?ARE impractical because of financial, logistical, or ethical ?considerations. In cases where an ideal randomized experiment cannot ?be performed, our knowledge of causal effects must come from ?non-randomized (i.e. observational) studies. This talk will focus on ?the proposal of an outcome-free three-stage procedure to estimate ?causal effects from non-randomized studies, which we call MITSS. ?First, we create subclasses that include observations from each group ?based on the covariates. Next, we independently estimate the response ?surface in each group using a flexible spline model. Lastly, multiple ?imputations of the missing potential outcomes are performed. A ?simulation analysis that resembles real life situations is conducted ?to compare MITSS to other commonly-used methods is carried out. In ?many of the conditions examined, MITSS produced a valid statistical ?procedure while providing a relatively precise point estimate and a ?relatively short interval estimate. We will demonstrate extensions of ?MITSS to estimate causal effects in multiple health services research ?studies. The extended procedures address common limitations ?encountered in many observational studies: CLEAR definition of causal ?effect estimands, non-collapsibility, confounded assignment ?mechanisms, treatment heterogeneity and dealing with forms of missing ?data other that due to the assignment mechanisms.

报告人简介：Roee Gutman, Department of Biostatistics, Brown ?University, Providence, RI

Zoom Meeting Room ID：849 963 1368

Password：YMSC