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

William Yuan (), Brett K. Beaulieu-Jones, Kun-Hsing Yu, Scott L. Lipnick, Nathan Palmer, Joseph Loscalzo, Tianxi Cai and Isaac S. Kohane ()
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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
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
Isaac S. Kohane: Department of Biomedical Informatics, Harvard Medical School

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-21390-2

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