Calibrating disease progression models using population data: a critical precursor to policy development in cancer control.
There are many more strategies for early detection of cancer than can be evaluated with randomized trials. Consequently, model-projected outcomes under different strategies can be useful for developing cancer control policy provided that the projections are representative of the population. To project population-representative disease progression outcomes and to demonstrate their value in assessing competing early detection strategies, we implement a model linking prostate-specific antigen (PSA) levels and prostate cancer progression and calibrate it to disease incidence in the US population. PSA growth is linear on the logarithmic scale with a higher slope after disease onset and with random effects on intercepts and slopes; parameters are estimated using data from the Prostate Cancer Prevention Trial. Disease onset, metastatic spread, and clinical detection are governed by hazard functions that depend on age or PSA levels; parameters are estimated by comparing projected incidence under observed screening and biopsy patterns with incidence observed in the Surveillance, Epidemiology, and End Results registries. We demonstrate implications of the model for policy development by projecting early detections, overdiagnosis, and mean lead times for PSA cutoffs 4.0 and 2.5 ng/mL and for screening ages 50-74 or 50-84. The calibrated model validates well, quantifies the tradeoffs involved across policies, and indicates that PSA screening with cutoff 4.0 ng/mL and screening ages 50-74 performs best in terms of overdiagnosis per early detection. The model produces representative outcomes for selected PSA screening policies and is shown to be useful for informing the development of sound cancer control policy.
Gulati
Roman
R
Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. rgulati@fhcrc.org
Inoue
Lurdes
L
Katcher
Jeffrey
J
Hazelton
William
W
Etzioni
Ruth
R
D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000097:blood; D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D001706:Biopsy; D003198:Computer Simulation; D003661:Decision Support Techniques; D018450:Disease Progression; D055088:Early Detection of Cancer / Q000379:methods*; D006291:Health Policy; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009362:Neoplasm Metastasis / Q000175:diagnosis; D009369:Neoplasms / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology / Q000517:prevention & control*; D011050:Policy Making; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology / Q000517:prevention & control
D015415:Biomarkers; D017430:Prostate-Specific Antigen