主 题: Causal Effect Estimation While Accounting for Uncertainty in Confounder and Effect Modifier Selection
报告人: Chi Wang (University of Kentucky)
时 间: 2017-05-25 10:00-11:00
地 点: 理科1号楼1114
Abstract: Confounder selection and adjustment are essential elements of assessing the causal effect of an exposure or treatment in observational studies. We propose and evaluate a Bayesian method to estimate average causal effects in studies with a large number of potential confounders, relatively few observations, likely interactions between confounders and the exposure of interest, and uncertainty on which confounders and interaction terms should be included. Our method is applicable across all exposures and outcomes that can be handled through generalized linear models. In this general setting, estimation of the average causal effect is different from estimation of the exposure coefficient in the outcome model due to non-collapsibility. We implement a Bayesian bootstrap procedure to integrate over the distribution of potential confounders and to estimate the causal effect. Our method permits estimation of both the overall population causal effect and effects in specified subpopulations, providing clear characterization of heterogeneous exposure effects that may vary considerably across different covariate profiles. Applications of the method are demonstrated using the US Medicare brain tumor data and the Genetic Analysis Workshop 19 (GAW19) sequencing data.
About the speaker: Chi Wang is Associate Professor in the Department of Biostatistics, College of Public Health and the Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center at the University of Kentucky. Prior to joining the University of Kentucky, he was Assistant Professor in the Department of Statistics at the University of California, Riverside. He received his B.S. and M.S. in Statistics from Peking University, and Ph.D. in Biostatistics from the Johns Hopkins University. His research interests include bioinformatics, causal inference, Bayesian methods, and survival analysis. He is also interested in applying biostatistical methods to cancer-related research projects.
