The use of modeling studies to illustrate the impact of decision choic
es in cancer treatment is becoming increasingly common. Decision maker
s 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 dat
a and be understandable to clinical experts. Assumptions with regard t
o population age, clinical stage, tumor differentiation and combinatio
ns of treatment modalities used initially, and for later management of
local relapse and distant metastases must all be considered. Difficul
ties abound because most data sources need some adjustment to avoid bi
as or to reflect current practice. Despite these difficulties, modelin
g studies may provide unique insights that are valuable for improving
prostate cancer decision making.