OBJECTIVES: The incidence of prostate cancer is increasing, as is the number of diagnostic and therapeutic interventions to manage this disease. We developed a Markov state-transition model--the Montreal Prostate Cancer Model--for improved forecasting of the health care requirements and outcomes associated with prostate cancer. We then validated the model by comparing its forecasted outcomes with published observations for various cohorts of men. METHODS: We combined aggregate data on the age-specific incidence of prostate cancer, the distribution of diagnosed tumours according to patient age, clinical stage and tumour grade, initial treatment, treatment complications, and progression rates to metastatic disease and death. Five treatments were considered: prostatectomy, radiation therapy, hormonal therapies, combination therapies and watchful waiting. The resulting model was used to calculate age-, stage-, grade- and treatment-specific clinical outcomes such as expected age at prostate cancer diagnosis and death, and metastasis-free, disease-specific and overall survival. RESULTS: We compared the model's forecasts with available cohort data from the Surveillance, Epidemiology and End Results (SEER) Program, based on over 59,000 cases of localized prostate cancer. Among the SEER cases, the 10-year disease-specific survival rates following prostatectomy for tumour grades 1, 2 and 3 were 98%, 91% and 76% respectively, as compared with the model's estimates of 96%, 92% and 84%. We also compared the model's forecasts with the grade-specific survival among patients from the Connecticut Tumor Registry (CTR). The 10-year disease-specific survival among the CTR cases for grades 1, 2 and 3 were 91%, 76% and 54%, as compared with the model's estimates of 91%, 73% and 37%. INTERPRETATION: The Montreal Prostate Cancer Model can be used to support health policy decision-making for the management of prostate cancer. The model can also be used to forecast clinical outcomes for individual men who have prostate cancer or are at risk of the disease.
OBJECTIVES: The incidence of prostate cancer is increasing, as is the number of diagnostic and therapeutic interventions to manage this disease. We developed a Markov state-transition model--the Montreal Prostate Cancer Model--for improved forecasting of the health care requirements and outcomes associated with prostate cancer. We then validated the model by comparing its forecasted outcomes with published observations for various cohorts of men. METHODS: We combined aggregate data on the age-specific incidence of prostate cancer, the distribution of diagnosed tumours according to patient age, clinical stage and tumour grade, initial treatment, treatment complications, and progression rates to metastatic disease and death. Five treatments were considered: prostatectomy, radiation therapy, hormonal therapies, combination therapies and watchful waiting. The resulting model was used to calculate age-, stage-, grade- and treatment-specific clinical outcomes such as expected age at prostate cancer diagnosis and death, and metastasis-free, disease-specific and overall survival. RESULTS: We compared the model's forecasts with available cohort data from the Surveillance, Epidemiology and End Results (SEER) Program, based on over 59,000 cases of localized prostate cancer. Among the SEER cases, the 10-year disease-specific survival rates following prostatectomy for tumour grades 1, 2 and 3 were 98%, 91% and 76% respectively, as compared with the model's estimates of 96%, 92% and 84%. We also compared the model's forecasts with the grade-specific survival among patients from the Connecticut Tumor Registry (CTR). The 10-year disease-specific survival among the CTR cases for grades 1, 2 and 3 were 91%, 76% and 54%, as compared with the model's estimates of 91%, 73% and 37%. INTERPRETATION: The Montreal Prostate Cancer Model can be used to support health policy decision-making for the management of prostate cancer. The model can also be used to forecast clinical outcomes for individual men who have prostate cancer or are at risk of the disease.
Authors: G S Gerber; R A Thisted; P T Scardino; H G Frohmuller; F H Schroeder; D F Paulson; A W Middleton; D B Rukstalis; J A Smith; P F Schellhammer; M Ohori; G W Chodak Journal: JAMA Date: 1996-08-28 Impact factor: 56.272
Authors: S A Grover; L Coupal; H Zowall; R Rajan; J Trachtenberg; M Elhilali; M Chetner; L Goldenberg Journal: CMAJ Date: 2000-04-04 Impact factor: 8.262
Authors: G L Lu-Yao; A L Potosky; P C Albertsen; J H Wasson; M J Barry; J E Wennberg Journal: J Natl Cancer Inst Date: 1996-02-21 Impact factor: 13.506
Authors: S A Grover; L Coupal; H Zowall; R Rajan; J Trachtenberg; M Elhilali; M Chetner; L Goldenberg Journal: CMAJ Date: 2000-04-04 Impact factor: 8.262