A0460
Title: What's the weight: Estimating controlled outcome differences in complex surveys for health disparities research
Authors: Stephen Salerno - Fred Hutchinson Cancer Center (United States) [presenting]
Emily Roberts - University of Iowa (United States)
Belinda Needham - University of Michigan (United States)
Tyler McCormick - University of Washington (United States)
Fan Li - Yale University (United States)
Bhramar Mukherjee - Yale University (United States)
Xu Shi - University of Michigan (United States)
Abstract: The motivation is the problem of estimating racial disparities in health outcomes, specifically the average controlled difference (ACD) in telomere length between Black and White individuals, using data from the National Health and Nutrition Examination Survey (NHANES). To do so, a propensity is built for race to properly adjust for other social determinants while characterizing the controlled effect of race on telomere length. Propensity score methods are employed with observational data as a tool to achieve covariate balance, but how to implement them in complex surveys is less studied - when the survey weights depend on the group variable under comparison (as the NHANES sampling scheme depends on self-reported race). Identification formulas are proposed to properly estimate the ACD in outcomes between Black and White individuals, with appropriate weighting for both covariate imbalance across the two racial groups and generalizability. Via extensive simulation, it is shown that the proposed methods outperform traditional analytic approaches in terms of bias, mean squared error, and coverage for our setting of interest. In used data, it is found that racial differences in telomere length between Black and White individuals attenuate after accounting for confounding by socioeconomic factors and utilizing appropriate propensity score and survey weighting techniques. Software to implement these methods and reproduce the results are found in the R package svycdiff.
