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Should Representativeness be Avoided? Reweighting the UK Biobank Corrects for Pervasive Selection Bias due to Volunteering

Author

Listed:
  • Sjoerd van Alten

    (Vrije Universiteit Amsterdam)

  • Benjamin Domingue
  • Jessica Faul

    (University of Michigan)

  • Titus Galama

    (University of Southern California)

  • Andries Marees

    (Vrije Universiteit Amsterdam)

Abstract

We investigate to what extent volunteer-based sampling of large-scale biobanks biases associations and estimate inverse probability (IP) weights to correct for such bias. Using the UK Biobank (UKB) as an example of a large-scale volunteer-based cohort, and population-representative data from the UK Census as a reference, we compare 21 bivariate associations in both data sets. Volunteer bias in all associations as naively estimated in the UKB is substantial, and in some cases leads to estimates of the wrong sign. For example, older individuals in the UKB report being in better health. Correcting for volunteer bias using IP weights is therefore advised. Applying IP weights reduces 87% of volunteer bias on average and suggests volunteer-based sampling reduces the effective sample size of the UKB to ∼32% of its original size. To aid the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design.

Suggested Citation

  • Sjoerd van Alten & Benjamin Domingue & Jessica Faul & Titus Galama & Andries Marees, 2023. "Should Representativeness be Avoided? Reweighting the UK Biobank Corrects for Pervasive Selection Bias due to Volunteering," Working Papers 2023-021, Human Capital and Economic Opportunity Working Group.
    • Handle: RePEc:hka:wpaper:2023-021
    Note: HI, MIP
    as

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    File URL: http://humcap.uchicago.edu/RePEc/hka/wpaper/van-Alten_Domingue_Faul_etal_2023_should-representativeness-be-avoided.pdf
    File Function: First version, June 12, 2023
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    More about this item

    Keywords

    volunteer bias; inverse probability weighting; sample size;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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