title: Optimization of PSA screening policies: a comparison of the patient and societal perspectives.
abstract: To estimate the benefit of PSA-based screening for prostate cancer from the patient and societal perspectives.
  
  
  
  A partially observable Markov decision process model was used to optimize PSA screening decisions. Age-specific prostate cancer incidence rates and the mortality rates from prostate cancer and competing causes were considered. The model trades off the potential benefit of early detection with the cost of screening and loss of patient quality of life due to screening and treatment. PSA testing and biopsy decisions are made based on the patient's probability of having prostate cancer. Probabilities are inferred based on the patient's complete PSA history using Bayesian updating.
  
  
  
  The results of all PSA tests and biopsies done in Olmsted County, Minnesota, from 1993 to 2005 (11,872 men and 50,589 PSA test results).
  
  
  
  Patients' perspective: to maximize expected quality-adjusted life years (QALYs); societal perspective: to maximize the expected monetary value based on societal willingness to pay for QALYs and the cost of PSA testing, prostate biopsies, and treatment.
  
  
  
  From the patient perspective, the optimal policy recommends stopping PSA testing and biopsy at age 76. From the societal perspective, the stopping age is 71. The expected incremental benefit of optimal screening over the traditional guideline of annual PSA screening with threshold 4.0 ng/mL for biopsy is estimated to be 0.165 QALYs per person from the patient perspective and 0.161 QALYs per person from the societal perspective. PSA screening based on traditional guidelines is found to be worse than no screening at all.
  
  
  
  PSA testing done with traditional guidelines underperforms and therefore underestimates the potential benefit of screening. Optimal screening guidelines differ significantly depending on the perspective of the decision maker.
authors.affiliation: Philips Research North America, Briarcliff Manor, NY (JZ)
authors.forename: Jingyu
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC (BTD)
authors.forename: Brian T
authors.initials: BT
authors.lastname: Denton
authors.affiliation: Department of Mechanical & Industrial Engineering, University of Massachusetts, Amherst, MA (HB)
authors.forename: Hari
authors.initials: H
authors.lastname: Balasubramanian
authors.affiliation: Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN (NDS)
authors.forename: Nilay D
authors.initials: ND
authors.lastname: Shah
authors.affiliation: Department of Surgery, School of Medicine, Duke University, Durham, NC (BAI)
authors.forename: Brant A
authors.initials: BA
authors.lastname: Inman
mesh_terms: D001294:Attitude to Health; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000097:blood*; D001707:Biopsy, Needle; D055088:Early Detection of Cancer; D042241:Early Diagnosis; D019983:Guideline Adherence; D006801:Humans; D007206:Individuality; D008297:Male; D008390:Markov Chains; D008910:Minnesota; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data; D011336:Probability; D011467:Prostate / Q000473:pathology; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000523:psychology; D011788:Quality of Life; D019057:Quality-Adjusted Life Years
chemical_list: D014408:Biomarkers, Tumor; D017430:Prostate-Specific Antigen