Simulation optimization of PSA-threshold based prostate cancer screening policies

Daniel J. Underwood, Jingyu Zhang, Brian T. Denton, Nilay D. Shah, Brant A. Inman

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


We describe a simulation optimization method to design PSA screening policies based on expected quality adjusted life years (QALYs). Our method integrates a simulation model in a genetic algorithm which uses a probabilistic method for selection of the best policy. We present computational results about the efficiency of our algorithm. The best policy generated by our algorithm is compared to previously recommended screening policies. Using the policies determined by our model, we present evidence that patients should be screened more aggressively but for a shorter length of time than previously published guidelines recommend.

Original languageEnglish (US)
Pages (from-to)293-309
Number of pages17
JournalHealth Care Management Science
Issue number4
StatePublished - Dec 2012


  • Genetic algorithm
  • Prostate cancer screening
  • Ranking and selection
  • Simulation optimization

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • General Health Professions


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