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

Manoj S Kambara1, Shivam Sharma2,3, John L Spouge4

  • 1National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland, USA.

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Summary
This summary is machine-generated.

Volunteer participant bias in the All of Us Research Program was characterized. Inverse probability weights were developed to mitigate bias, improving cohort representativeness for epidemiological studies.

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Area of Science:

  • Epidemiology
  • Population Health
  • Genetics

Background:

  • Large-scale biobanks depend on volunteers, potentially introducing bias and limiting the generalizability of findings.
  • The All of Us Research Program cohort exhibits demographic and health differences compared to the general US population.

Purpose of the Study:

  • To characterize volunteer participant bias within the All of Us Research Program cohort.
  • To develop and provide inverse probability (IP) weights to mitigate identified biases and enhance cohort representativeness.

Main Methods:

  • Comparison of the All of Us cohort demographics, lifestyle, and health status against a nationally representative database.
  • Development of inverse probability (IP) weights using the comparative data.

Main Results:

  • The All of Us cohort is older, more female, more educated, insured, less White, and less healthy than the US population.
  • IP weights successfully eliminated demographic and lifestyle biases and reduced disease prevalence differences.
  • IP weights influenced genetic associations with type 2 diabetes across diverse ancestries.

Conclusions:

  • Inverse probability weighting is an effective method to address volunteer bias in the All of Us cohort.
  • The developed IP weights serve as a valuable resource to improve the external validity of research using the All of Us data.
  • Mitigating bias enhances the reliability of epidemiological and genetic findings from population biobanks.