Epidemiology and statistical methods in prediction of patient outcome

David G. Bostwick*, Jan Adolfsson, Harry B. Burke, Jan Erik Damber, Hartwig Huland, Michele Pavone-Macaluso, David J. Waters

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Substantial gaps exist in the data of the assessment of risk and prognosis that limit our understanding of the complex mechanisms that contribute to the greatest cancer epidemic, prostate cancer, of our time. This report was prepared by an international multidisciplinary committee of the World Health Organization to address contemporary issues of epidemiology and statistical methods in prostate cancer, including a summary of current risk assessment methods and prognostic factors. Emphasis was placed on the relative merits of each of the statistical methods available. We concluded that: 1. An international committee should be created to guide the assessment and validation of molecular biomarkers. The goal is to achieve more precise identification of those who would benefit from treatment. 2. Prostate cancer is a predictable disease despite its biologic heterogeneity. However, the accuracy of predicting it must be improved. We expect that more precise statistical methods will supplant the current staging system. The simplicity and intuitive ease of using the current staging system must be balanced against the serious compromise in accuracy for the individual patient. 3. The most useful new statistical approaches will integrate molecular biomarkers with existing prognostic factors to predict conditional life expectancy (i.e. the expected remaining years of a patient's life) and take into account all-cause mortality.

Original languageEnglish
Pages (from-to)94-110
Number of pages17
JournalScandinavian Journal of Urology and Nephrology, Supplement
Issue number216
StatePublished - May 2005
Externally publishedYes


  • Artificial neural networks
  • Biomarkers
  • Diagnosis
  • Molecular biology
  • Prostate cancer
  • Regression models
  • Risk
  • Statistics


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