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Activity Number: 539
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #316505
Title: Causal Modeling for Disparity in Treatment Response
Author(s): Chen-Pin Wang*
Companies: Department of Epidemiology and Biostatistics
Keywords: disparity ; causal modeling ; principal stratification ; type 2 diabetes ; prostate cancer
Abstract:

I propose a causal modeling for assessing disparity in treatment response (DITR) to an endpoint outcome Y accounting for treatment response to an intermediate variable M. DITR refers to treatment effect differs between (e.g., ethnic) groups. The goal of the method is to estimate the difference in treatment response to Y should the 'modifiable covariates' between groups be balanced. Two types of modifiable covariates are considered: 1) those associated with between group difference in treatment response to M, and 2) those associated with between group difference in treatment response to Y. Both conditional dependence and independence between Y and M given covariates are considered. The proposed model integrates the covariate weighting method by Duan et al (Stat. Med. 2008) and principal stratification (identify principal strata associated with M). The proposed method is evaluated by simulations. Its application is demonstrated using a study that assesses the differential impact of metformin on prostate cancer incidence between Hispanic and non-Hispanic men with type 2 diabetes. I will discuss how correct the conclusion of DITR depends on correct specification of the modifiabl


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