Increasing Representativeness in the All of Us Cohort Using Inverse Probability Weighting

Abstract

Large-scale population biobanks rely on volunteer participants, which may introduce biases that compromise the external validity of epidemiological studies. We characterized the volunteer participant bias for the All of Us Research Program cohort and developed a set of inverse probability (IP) weights that can be used to mitigate this bias. The All of Us cohort is older, more female, more educated, more likely to be covered by health insurance, less White, less likely to drink or smoke, and less healthy compared to the US population. IP weights developed via comparison of a nationally representative database eliminated the observed biases for all demographic and lifestyle characteristics and reduced the observed disease prevalence differences. IP weights also impact genetic associations with type 2 diabetes across diverse ancestry cohorts. We provide our IP weights as a community resource to increase the representativeness and external validity of the All of Us cohort.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Open Access funding provided by the National Institutes of Health (NIH) MK, and LMR supported by the Division of Intramural Research of the National Institute on Minority Health and Health Disparities at the National Institutes of Health (Award Number: 1ZIAMD000018) to LMR; National Institutes of Health Distinguished Scholars Program to LMR; SS supported by the Georgia Tech Bioinformatics Graduate Program; JL supported by the Intramural Research Program of the National Institutes of Health, National Library of Medicine, and National Center for Biotechnology Information; and IHRC-Georgia Tech Applied Bioinformatics Laboratory (Award Number: RF383) to IKJ.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The nationally representative database used to develop weights was the 2017 - March 2020 National Health and Nutrition Examination Survey (NHANES). This data is free to access and publicly available at: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?Cycle=2017-2020 We used version 7 of the All of Us Controlled Tier Dataset, which can be accessed and analyzed from the Researcher Workbench by registered users: https://www.researchallofus.org/data-tools/workbench/

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