Abstract Number:
3655
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Stephen Salerno (1), Emily Roberts (2), Belinda Needham (3), Tyler McCormick (4), Bhramar Mukherjee (3), Xu Shi (3)
Institutions:
(1) Fred Hutchinson Cancer Center, Seattle, WA, (2) University of Iowa, Iowa City, IA, (3) University of Michigan, Ann Arbor, MI, (4) University of Washington, Seattle, WA
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
A basic descriptive question is whether differences between groups exist based on levels of a discrete covariate (e.g., racial disparities in health outcomes). When this covariate is correlated with other factors related to the outcome, direct comparisons may yield misleading patterns without appropriate adjustment. Propensity scores are often employed as tools to achieve covariate balance in observational data, but their use in certain complex survey settings remains an open challenge. We focus on a specific problem – when sample selection depends on group membership. We propose novel estimands to study the average controlled difference in outcomes between groups. In simulation, our methods outperform traditional approaches, with less bias, lower mean squared error, and nominal coverage rates. Our motivation uses data from the National Health and Nutrition Examination Survey to study the interplay of self-reported race and social determinants of health on telomere length. We find that estimated differences in telomere length between Black and White individuals are attenuated compared to methods that do not appropriately account for simultaneous confounding and selection biases.
Keywords:
Confounding Bias|Average Controlled Difference|Complex Surveys|NHANES|Racial Disparities|Telomere Length
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Miscellaneous
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