Substantial gaps exist in our ability to accurately predict prognosis, and these gaps limit our understanding of the complex mechanisms that contribute to the greatest cancer epidemic of our time, prostate cancer. This review addresses contemporary epidemiologic and biostatistical issues in prostate cancer. It covers the science of outcome prediction and biomarker evaluation, recognition of the need to combine biomarkers to improve the accuracy of our outcome estimates and an analysis of current outcome assessment methods, including the TNM staging system and multivariate regression models. The simplicity and intuitive ease of the current TNM staging system must be balanced against its serious limitations in predictive accuracy and its loss of clinical utility. Statistical regression methods are required as we move to the new era of personalized medicine. We must implement statistical approaches that integrate the new molecular biomarkers with existing prognostic biomarkers to accurately predict which patients require treatment and to determine the optimal therapy.