title: Clinical management and burden of prostate cancer: a Markov Monte Carlo model.
abstract: BACKGROUND
  Prostate cancer (PCa) is the most common non-skin cancer among men in developed countries. Several novel treatments have been adopted by healthcare systems to manage PCa. Most of the observational studies and randomized trials on PCa have concurrently evaluated fewer treatments over short follow-up. Further, preceding decision analytic models on PCa management have not evaluated various contemporary management options. Therefore, a contemporary decision analytic model was necessary to address limitations to the literature by synthesizing the evidence on novel treatments thereby forecasting short and long-term clinical outcomes.
  
  
  OBJECTIVE
  To develop and validate a Markov Monte Carlo model for the contemporary clinical management of PCa, and to assess the clinical burden of the disease from diagnosis to end-of-life.
  
  
  METHODS
  A Markov Monte Carlo model was developed to simulate the management of PCa in men 65 years and older from diagnosis to end-of-life. Health states modeled were: risk at diagnosis, active surveillance, active treatment, PCa recurrence, PCa recurrence free, metastatic castrate resistant prostate cancer, overall and PCa death. Treatment trajectories were based on state transition probabilities derived from the literature. Validation and sensitivity analyses assessed the accuracy and robustness of model predicted outcomes.
  
  
  RESULTS
  Validation indicated model predicted rates were comparable to observed rates in the published literature. The simulated distribution of clinical outcomes for the base case was consistent with sensitivity analyses. Predicted rate of clinical outcomes and mortality varied across risk groups. Life expectancy and health adjusted life expectancy predicted for the simulated cohort was 20.9 years (95%CI 20.5-21.3) and 18.2 years (95% CI 17.9-18.5), respectively.
  
  
  CONCLUSIONS
  Study findings indicated contemporary management strategies improved survival and quality of life in patients with PCa. This model could be used to compare long-term outcomes and life expectancy conferred of PCa management paradigms.
authors.affiliation: Department of Surgery, Division of Urology, McGill University, Montreal, Quebec, Canada.
authors.forename: Chiranjeev
authors.initials: C
authors.lastname: Sanyal
authors.affiliation: Department of Surgery, Division of Urology, McGill University, Montreal, Quebec, Canada; Research Institute of McGill University Health Center, Montreal, Quebec, Canada.
authors.forename: Armen
authors.initials: A
authors.lastname: Aprikian
authors.affiliation: Department of Surgery, Division of Urology, McGill University, Montreal, Quebec, Canada; Department of Radiation Oncology, McGill University Health Center, Montreal, Quebec, Canada.
authors.forename: Fabio
authors.initials: F
authors.lastname: Cury
authors.affiliation: Department of Surgery, Division of Urology, McGill University, Montreal, Quebec, Canada; Research Institute of McGill University Health Center, Montreal, Quebec, Canada.
authors.forename: Simone
authors.initials: S
authors.lastname: Chevalier
authors.affiliation: Department of Surgery, Division of Urology, McGill University, Montreal, Quebec, Canada; Research Institute of McGill University Health Center, Montreal, Quebec, Canada.
authors.forename: Alice
authors.initials: A
authors.lastname: Dragomir
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D018572:Disease-Free Survival; D006304:Health Status; D006801:Humans; D008017:Life Expectancy; D008297:Male; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D009364:Neoplasm Recurrence, Local; D017063:Outcome Assessment, Health Care; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000401:mortality / Q000473:pathology* / Q000628:therapy; D012306:Risk