Two-Stage Biomarker Protocols for Improving the Precision of Early Detection of Prostate Cancer

Christine L. Barnett, Scott A. Tomlins, Daniel J. Underwood, John T. Wei, Todd M. Morgan, James E. Montie and Brian T. Denton

Medical Decision Making, 2017, vol. 37, issue 7, 815-826

Abstract: Background. New cancer biomarkers are being discovered at a rapid pace; however, these tests vary in their predictive performance characteristics, and it is unclear how best to use them. Methods. We investigated 2-stage biomarker-based screening strategies in the context of prostate cancer using a partially observable Markov model to simulate patients’ progression through prostate cancer states to mortality from prostate cancer or other causes. Patients were screened every 2 years from ages 55 to 69. If the patient’s serum prostate-specific antigen (PSA) was over a specified threshold in the first stage, a second stage biomarker test was administered. We evaluated design characteristics for these 2-stage strategies using 7 newly discovered biomarkers as examples. Monte Carlo simulation was used to estimate the number of screening biopsies, prostate cancer deaths, and quality-adjusted life-years (QALYs) per 1000 men. Results. The all-cancer biomarkers significantly underperformed the high-grade cancer biomarkers in terms of QALYs. The screening strategy that used a PSA threshold of 2 ng/mL and a second biomarker test with high-grade sensitivity and specificity of 0.86 and 0.62, respectively, maximized QALYs. This strategy resulted in a prostate cancer death rate within 1% of using PSA alone with a threshold of 2 ng/mL, while reducing the number of biopsies by 20%. Sensitivity analysis suggests that the results are robust with respect to variation in model parameters. Conclusions. Two-stage biomarker screening strategies using new biomarkers with risk thresholds optimized for high-grade cancer detection may increase quality-adjusted survival and reduce unnecessary biopsies.

Keywords: Biomarkers; Prostate Cancer; Markov Model; Simulation (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:37:y:2017:i:7:p:815-826

DOI: 10.1177/0272989X17696996

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