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Prostate cancer is a slowly progressing disease that has a variety of treatment options. The choice of treatment for a particular patient is influenced by several factors. Each patient must consider his own tolerance level regarding treatment-specific clinical outcomes. One patient may choose a treatment that has a high risk of some adverse side effect if there is a low risk of disease progression. A similar patient may elect to forgo such treatment due to the lower quality of life that they may develop once the treatment is given. Prediction tools that estimate the risk of a clinical outcome are an invaluable resource to the patient in this context.

This chapter reviews statistical methods that are used to develop prediction tools for risk stratification and prediction of clinical outcomes. Using these sophisticated techniques, many prediction tools have been developed that provide patient-specific risk assessment with respect to clinical outcomes that can occur throughout the course of prostate cancer. This chapter will provide a review of some of these models in an attempt to provide both the treating physician and patient the most current prediction models available to decide an optimal strategy for prostate cancer management.


Prior to prostate cancer diagnosis, it is of most interest in determining the likelihood that a patient has prostate cancer and if prostate cancer is present, if it is of an indolent or aggressive nature. The treating physician and patient must then decide which treatment options are available and which will provide the best possible set of health outcomes for the patient. Each treatment must be assessed with regard to its effect on the time to biochemical progression (sometimes used as a surrogate measure for disease progression), the time to metastatic disease or the time to disease-specific death. Due to the indolent nature of some prostate cancers, these health outcomes may take several years to develop and treatment side-effects must also be considered. Side-effects, such as urinary incontinence, bowel dysfunction, erectile dysfunction and irritative bladder symptoms, may significantly lower a patient’s quality of life after treatment and his risk of side-effects must also be considered in conjunction with the risk of developing other clinical outcomes. This chapter will review prediction models that have been developed for some of these outcomes.


Patients are inherently different with regard to their medical history. Predicting a patient’s risk of developing some health outcome must take account of patient heterogeneity to obtain reliable estimates of risk. Factors that are strongly associated with the development of an outcome are called ‘risk factors’. However, even strong risk factors may not be useful for risk assessment if their prevalence is extremely low [1] or if they are not routinely collected. These limitations must be considered in the development of any prediction tool.


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