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

Sjoerd van Alten (), Benjamin Domingue, Jessica Faul, Titus Galama () and Andries Marees
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Sjoerd van Alten: Vrije Universiteit Amsterdam
Jessica Faul: University of Michigan
Titus Galama: University of Southern California
Andries Marees: Vrije Universiteit Amsterdam

No 2023-021, Working Papers from Human Capital and Economic Opportunity Working Group

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.

Keywords: volunteer bias; inverse probability weighting; sample size (search for similar items in EconPapers)
JEL-codes: C25 C83 I18 (search for similar items in EconPapers)
Date: 2023-08
New Economics Papers: this item is included in nep-dem
Note: HI, MIP
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