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

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

SUBMITTER: Aleshin-Guendel S 

PROVIDER: S-EPMC8279086 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature