A Latent Disease Model to Reduce Detection Bias in Cancer Risk Prediction Studies. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Aleshin-Guendel, Serge; Lange, Jane; Goodman, Phyllis; Weiss, Noel S.; Etzioni, Ruth
• Other Names: Scheier Lawrence M. guest-editor.; Flaherty Brian P. guest-editor.
• Is Part Of: Evaluation & the health professions. Volume 44:Issue 1(2021)
• Page Start: 42
• Page End: 49
• Publication Date: 2021-03
• Subjects: detection bias -- cancer screening -- latent model -- risk prediction -- disease surveillance; Medical care -- Evaluation -- Periodicals; Medicine -- Ability testing -- Periodicals; 362.105
• DOI: 10.1177/0163278720984203
• Languages: English
• ISSNs: 0163-2787

Abstract

In studies of cancer risk, detection bias arises when risk factors are associated with screening patterns, affecting the likelihood and timing of diagnosis. To eliminate detection bias in a screened cohort, we propose modeling the latent onset of cancer and estimating the association between risk factors and onset rather than diagnosis. We apply this framework to estimate the increase in prostate cancer risk associated with black race and family history using data from the SELECT prostate cancer prevention trial, in which men were screened and biopsied according to community practices. A positive family history was associated with a hazard ratio (HR) of prostate cancer onset of 1.8, lower than the corresponding HR of prostate cancer diagnosis (HR = 2.2). This result comports with a finding that men in SELECT with a family history were more likely to be biopsied following a positive PSA test than men with no family history. For black race, the HRs for onset and diagnosis were similar, consistent with similar patterns of screening and biopsy by race. If individual screening and diagnosis histories are available, latent disease modeling can be used to decouple risk of disease from risk of disease diagnosis and reduce detection bias.