What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research

TL;DR

This paper develops new methods for accurately estimating racial disparities in health outcomes using complex survey data, addressing challenges posed by survey weights and covariate imbalance, and demonstrates their effectiveness through simulations and real data analysis.

Contribution

It introduces identification formulas and estimation techniques for controlled outcome differences in complex surveys, improving bias and coverage over traditional methods.

Findings

Racial differences in telomere length decrease after adjusting for socioeconomic factors.

Proposed methods outperform traditional approaches in bias and mean squared error.

Software implementation is available in the R package svycdiff.

Abstract

In this work, we are motivated by 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, we build a propensity for race to properly adjust for other social determinants while characterizing the controlled effect of race on telomere length. Propensity score methods are broadly employed with observational data as a tool to achieve covariate balance, but how to implement them in complex surveys is less studied - in particular, when the survey weights depend on the group variable under comparison (as the NHANES sampling scheme depends on self-reported race). We propose identification formulas 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, we show that our proposed methods outperform traditional analytic approaches in terms of bias, mean squared error, and coverage when estimating the ACD for our setting of interest. In our data, we find that evidence of racial differences in telomere length between Black and White individuals attenuates after accounting for confounding by socioeconomic factors and utilizing appropriate propensity score and survey weighting techniques. Software to implement these methods and code to reproduce our results can be found in the R package svycdiff, available through the Comprehensive R Archive Network (CRAN) at cran.r-project.org/web/packages/svycdiff/, or in a development version on GitHub at github.com/salernos/svycdiff.