TY - JOUR
T1 - A Latent Disease Model to Reduce Detection Bias in Cancer Risk Prediction Studies
AU - Aleshin-Guendel, Serge
AU - Lange, Jane
AU - Goodman, Phyllis
AU - Weiss, Noel S.
AU - Etzioni, Ruth
N1 - Publisher Copyright:
© The Author(s) 2021.
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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.
KW - cancer screening
KW - detection bias
KW - disease surveillance
KW - latent model
KW - risk prediction
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U2 - 10.1177/0163278720984203
DO - 10.1177/0163278720984203
M3 - Article
C2 - 33506704
AN - SCOPUS:85100506053
SN - 0163-2787
VL - 44
SP - 42
EP - 49
JO - Evaluation and the Health Professions
JF - Evaluation and the Health Professions
IS - 1
ER -