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Temporal bias in case-control design: preventing reliable predictions of the future

Author

Listed:
  • William Yuan

    (Department of Biomedical Informatics, Harvard Medical School)

  • Brett K. Beaulieu-Jones

    (Department of Biomedical Informatics, Harvard Medical School)

  • Kun-Hsing Yu

    (Department of Biomedical Informatics, Harvard Medical School)

  • Scott L. Lipnick

    (Department of Biomedical Informatics, Harvard Medical School
    Harvard University
    Center for Assessment Technology and Continuous Health, Massachusetts General Hospital)

  • Nathan Palmer

    (Department of Biomedical Informatics, Harvard Medical School)

  • Joseph Loscalzo

    (Department of Medicine, Brigham and Women’s Hospital)

  • Tianxi Cai

    (Department of Biomedical Informatics, Harvard Medical School
    Division of Data Sciences, VA Boston Healthcare System
    Department of Biostatistics, Harvard T. H. Chan School of Public Health)

  • Isaac S. Kohane

    (Department of Biomedical Informatics, Harvard Medical School)

Abstract

One of the primary tools that researchers use to predict risk is the case-control study. We identify a flaw, temporal bias, that is specific to and uniquely associated with these studies that occurs when the study period is not representative of the data that clinicians have during the diagnostic process. Temporal bias acts to undermine the validity of predictions by over-emphasizing features close to the outcome of interest. We examine the impact of temporal bias across the medical literature, and highlight examples of exaggerated effect sizes, false-negative predictions, and replication failure. Given the ubiquity and practical advantages of case-control studies, we discuss strategies for estimating the influence of and preventing temporal bias where it exists.

Suggested Citation

  • William Yuan & Brett K. Beaulieu-Jones & Kun-Hsing Yu & Scott L. Lipnick & Nathan Palmer & Joseph Loscalzo & Tianxi Cai & Isaac S. Kohane, 2021. "Temporal bias in case-control design: preventing reliable predictions of the future," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21390-2
    DOI: 10.1038/s41467-021-21390-2
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    Cited by:

    1. Zhichao Yang & Avijit Mitra & Weisong Liu & Dan Berlowitz & Hong Yu, 2023. "TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Iain S. Forrest & Ben O. Petrazzini & Áine Duffy & Joshua K. Park & Anya J. O’Neal & Daniel M. Jordan & Ghislain Rocheleau & Girish N. Nadkarni & Judy H. Cho & Ashira D. Blazer & Ron Do, 2023. "A machine learning model identifies patients in need of autoimmune disease testing using electronic health records," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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