Literature DB >> 8283192

A Markov model of the natural history of prostate cancer.

M E Cowen1, M Chartrand, W F Weitzel.   

Abstract

The objective of this study was to lay a foundation for future cost-benefit analyses evaluating the public health impact of treatment and screening protocols for prostate cancer. Specifically we wanted to define the relative impact on cancer-specific mortality rates of the individual epidemiological components: pathological incidences by age groups, cancer progression rates, and the effect of competing causes of death, assuming expectant management (i.e. no definitive treatment). A biological model of prostate cancer incidence and progression was converted into a standard Markov tree where competing causes of death could occur. Weighted averages of progression rates were obtained from clinical studies. Separate cohorts of 30 year old black and white men were followed for 50 years. The model yielded cancer-specific mortality rates, overall mortality rates, and pathologic prevalences for both white and black males, consistent with the literature. Sensitivity analyses showed that of all the parameters studied, the pathological incidence of cancer in men under 50 years of age had the greatest impact on the cancer-specific mortality rates. Also important was the annual probability of progression of A1 lesions. However the other parameters including pathological incidence in older males, and progression from locally-extensive to metastatic lesions had much smaller effects. In summary, this model correlates the clinical literature with the epidemiology of prostate cancer and can be used for further decision analyses. We recommend that future research be done to more precisely quantify the pathological incidence of prostate cancer in men under 50-60 years of age. More certainty is also needed before generalizing the results of relatively small A1 series to millions of men, since A1 progression rates critically affect the eventual cancer-specific mortality. Enough uncertainty remains at this point however, that we cannot advocate widespread screening for prostate cancer until its merit be demonstrated either by the definitive long term study, or by examination of costs and quality-of-life-adjusted benefits.

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Year:  1994        PMID: 8283192     DOI: 10.1016/0895-4356(94)90029-9

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  7 in total

Review 1.  Calibration methods used in cancer simulation models and suggested reporting guidelines.

Authors:  Natasha K Stout; Amy B Knudsen; Chung Yin Kong; Pamela M McMahon; G Scott Gazelle
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

Review 2.  Age-related racial disparities in prostate cancer patients: A systematic review.

Authors:  Ting He; C Daniel Mullins
Journal:  Ethn Health       Date:  2016-10-05       Impact factor: 2.772

3.  The prognosis of stage A patients treated with the antiandrogen chlormadinone acetate.

Authors:  Y Kubota; T Nakada; I Sasagawa; H Yanai; K Itoh; H Suzuki
Journal:  Int Urol Nephrol       Date:  1999       Impact factor: 2.370

4.  The clinical burden of prostate cancer in Canada: forecasts from the Montreal Prostate Cancer Model.

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

5.  The economic burden of prostate cancer in Canada: forecasts from the Montreal Prostate Cancer Model.

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

6.  Quantifying the role of PSA screening in the US prostate cancer mortality decline.

Authors:  Ruth Etzioni; Alex Tsodikov; Angela Mariotto; Aniko Szabo; Seth Falcon; Jake Wegelin; Dante DiTommaso; Kent Karnofski; Roman Gulati; David F Penson; Eric Feuer
Journal:  Cancer Causes Control       Date:  2007-11-20       Impact factor: 2.506

7.  Clinical management and burden of prostate cancer: a Markov Monte Carlo model.

Authors:  Chiranjeev Sanyal; Armen Aprikian; Fabio Cury; Simone Chevalier; Alice Dragomir
Journal:  PLoS One       Date:  2014-12-04       Impact factor: 3.240

  7 in total

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