82 – Comparative Effectiveness Research and Causal Modeling
Estimation of Disease-Specific Health Care Costs Using Causal Inference Framework
Irina Bondarenko
University of Michigan
Trivellore Raghunathan
University of Michigan
Estimation of the costs attributable to various diseases is needed to understand the structure of health care spending and to develop strategies to improve efficiency of health care. This estimation problem can be framed as a causal inference from an observational study. This article will develop and compare various methods for causal inference. We investigate four methods--propensity score stratification, and three approaches based on multiple imputations of counterfactuals, for estimating disease-specific costs. For multiple imputation inference, we use three approaches: (1) Parametric approach using log-normal distribution; (2) Tukey's gh-distribution(GH) on the original scale; and (3) Approximate Bayesian Bootstrap (ABB). Data from the Medicare Current Beneficiary Survey (MCBS) is used to illustrate the methodology. We also evaluate the repeated sampling properties of the estimates through a simulation study.