To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice.

Autor:	Salvatore M; Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Center for Precision Health Data Science, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States., Kundu R; Center for Precision Health Data Science, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States., Shi X; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States., Friese CR; Rogel Cancer Center, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Center for Improving Patient and Population Health, School of Nursing, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Department of Health Management and Policy, University of Michigan, Ann Arbor, MI 48109-2029, United States., Lee S; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Graduate School of Data Science, Seoul National University, Gwanak-gu, Seoul, Republic of Korea., Fritsche LG; Center for Precision Health Data Science, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Rogel Cancer Center, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109-2029, United States., Mondul AM; Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Rogel Cancer Center, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109-2029, United States., Hanauer D; Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI 48109-2054, United States., Pearce CL; Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Rogel Cancer Center, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109-2029, United States., Mukherjee B; Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Center for Precision Health Data Science, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States.; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 Jun 20; Vol. 31 (7), pp. 1479-1492.
DOI:	10.1093/jamia/ocae098
Abstrakt:	Objectives: To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data. Materials and Methods: We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n = 244 071), Michigan Genomics Initiative (MGI; n = 81 243), and UK Biobank (UKB; n = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results. Results: For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates. Discussion: Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis. Conclusion: EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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