Abstract
The use of modeling studies to illustrate the impact of decision choices in cancer treatment is becoming increasingly common. Decision makers should have some understanding of modeling terminology and issues to evaluate such studies. To be useful, prostate cancer treatment models must be based on acceptable structural assumptions, contain valid data and be understandable to clinical experts. Assumptions with regard to population age, clinical stage, tumor differentiation and combinations of treatment modalities used initially, and for later management of local relapse and distant metastases must all be considered. Difficulties abound because most data sources need some adjustment to avoid bias or to reflect current practice. Despite these difficulties, modeling studies may provide unique insights that are valuable for improving prostate cancer decision making.
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