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A System Dynamics Model of Serum Prostate-Specific Antigen Screening for Prostate Cancer
Author(s) -
Anton M. Palma,
David W. Lounsbury,
Nicolas F. Schlecht,
Ilir Agalliu
Publication year - 2015
Publication title -
american journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.33
H-Index - 256
eISSN - 1476-6256
pISSN - 0002-9262
DOI - 10.1093/aje/kwv262
Subject(s) - prostate specific antigen , prostate , prostate cancer , medicine , antigen , cancer , oncology , immunology
Since 2012, US guidelines have recommended against prostate-specific antigen (PSA) screening for prostate cancer. However, evidence of screening benefit from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening trial and the European Randomized Study of Screening for Prostate Cancer has been inconsistent, due partly to differences in noncompliance and contamination. Using system dynamics modeling, we replicated the PLCO trial and extrapolated follow-up to 20 years. We then simulated 3 scenarios correcting for contamination in the PLCO control arm using Surveillance, Epidemiology, and End Results (SEER) incidence and survival data collected prior to the PSA screening era (scenario 1), SEER data collected during the PLCO trial period (1993-2001) (scenario 2), and data from the European trial's control arm (1991-2005) (scenario 3). In all scenarios, noncompliance was corrected using incidence and survival rates for men with screen-detected cancer in the PLCO screening arm. Scenarios 1 and 3 showed a benefit of PSA screening, with relative risks of 0.62 (95% confidence interval: 0.53, 0.72) and 0.70 (95% confidence interval: 0.59, 0.83) for cancer-specific mortality after 20 years, respectively. In scenario 2, however, there was no benefit of screening. This simulation showed that after correcting for noncompliance and contamination, there is potential benefit of PSA screening in reducing prostate cancer mortality. It also demonstrates the utility of system dynamics modeling for synthesizing epidemiologic evidence to inform public policy.

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