A Latent Disease Model to Reduce Detection Bias in Cancer Risk Prediction Studies

Serge Aleshin-Guendel, Jane Lange, Phyllis Goodman, Noel S. Weiss, Ruth Etzioni

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)42-49
Number of pages8
JournalEvaluation and the Health Professions
Volume44
Issue number1
DOIs
StatePublished - Mar 2021
Externally publishedYes

Keywords

  • cancer screening
  • detection bias
  • disease surveillance
  • latent model
  • risk prediction

ASJC Scopus subject areas

  • Health Policy

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