Cer with Ever-Increasing Amounts of Administrative Data: Bayesian Methods for Confounding Uncertainty and Heterogeneous Treatment Effects

• Activity Number: 588
• Type: Topic Contributed
• Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Corwin Zigler*+
• Companies: Harvard School of Public Health
• Keywords: Administrative data ; Causal inference ; Comparative effectiveness ; selection bias ; treatment effect heterogeneity

Abstract

Comparative effectiveness research depends heavily on the analysis of a rapidly expanding universe of observational data made possible by the growing integration of health care delivery, the dissemination of electronic medical records systems, the development of clinical registries data, and the increasing global connectedness created by widely available networks. Despite extraordinary opportunities for research aimed at improving value in health care, a critical barrier to progress relates to the lack of sound statistical methods that can address the multiple facets of estimating treatment effects in large, process-of-care data bases with little a prior knowledge about confounding, subpopulations experiencing heterogeneous treatment effects, and the decisions that gave rise to selection criteria. To address these barriers, we discuss methods for estimating treatment effects that account for uncertainty in the following three domains: 1) which of a high-dimensional set of observed covariates are confounders required to estimate causal effects; 2) which (if any) subgroups of the study population experience treatment effects that are heterogeneous with respect to observed factors;