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Updated: Jun 12, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Calibrating disease progression models using population data: a critical precursor to policy development in cancer
Roman Gulati1, Lurdes Inoue, Jeffrey Katcher
1Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. rgulati@fhcrc.org
Biostatistics (Oxford, England)
|June 10, 2010
Summary
Modeling prostate cancer progression helps assess early detection strategies. PSA screening with a 4.0 ng/mL cutoff and ages 50-74 best balances early detection with overdiagnosis.
Area of Science:
- Oncology
- Biostatistics
- Health Policy
Background:
- Evaluating numerous cancer early detection strategies via randomized trials is challenging.
- Mathematical modeling can project outcomes for policy development when population representativeness is ensured.
Purpose of the Study:
- To implement and calibrate a population-representative model linking prostate-specific antigen (PSA) levels and prostate cancer progression.
- To demonstrate the model's utility in assessing competing early detection strategies for prostate cancer.
Main Methods:
- A model was developed linking PSA levels and prostate cancer progression, calibrated to US population incidence.
- Parameters were estimated using data from the Prostate Cancer Prevention Trial and Surveillance, Epidemiology, and End Results (SEER) registries.
- The model projected outcomes for different PSA screening cutoffs (4.0 and 2.5 ng/mL) and age ranges (50-74 and 50-84).
Main Results:
- The calibrated model showed good validation and quantified tradeoffs between early detection and overdiagnosis.
- PSA screening with a 4.0 ng/mL cutoff and screening ages 50-74 demonstrated the best performance regarding overdiagnosis per early detection.
- The model projected population-representative outcomes for selected PSA screening policies.
Conclusions:
- The developed model is a valuable tool for informing cancer control policy regarding PSA screening.
- It effectively quantifies the trade-offs inherent in different screening strategies.
- The findings suggest specific parameters for PSA screening that optimize early detection while minimizing overdiagnosis.
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