The primary endpoint of this study (decisional regret) and secondary endpoints (treatment satisfaction, decisional conflict, and current health state utility) will be assessed by means of a patient-reported questionnaire using validated instruments assessed at 6, 12 and 24 months after randomization. A decision analysis model will be developed using a Markov modeling approach. Using Treeage Data® pro suite, the model will incorporate cancer outcomes, treatment-related morbidity probabilities derived from nomograms, and patient-specific utilities to estimate the quality-adjusted disease-free survival for each treatment option. In the model, all patients will begin with localized prostate cancer with no evidence of metastases. A 6-month transition cycle will be used for the Markov model. Bootstrap simulation with replacement will be utilized to derive mean utilities and 95% confidence intervals. Sensitivity analyses will be performed by varying the disease progression rates and utilities within their 95% confidence intervals. To investigate if the decision analytic model, compared to standard interactions alone, will influence treatment choice and lead to higher quality treatment decisions that are congruent with a patient's values, an intent-to-treat analysis will be conducted on patients who did and did not receive the decision analysis recommendation with decisional regret as the primary endpoint. In a planned secondary analysis, we will analyze both arms of our trial and test the hypothesis that a patients who chooses a treatment strategy that did not appear to maximize his quality-adjusted disease-free survival (i.e. not the one the decision analysis would have yielded) will be at increased risk of regret. This will be conducted by identifying patients in both arms who choose the treatment that is also recommended by the decision model. The probability of selecting the decision analytic treatment strategy (which is also the probability of selecting the highest quality treatment decision congruent with a patient's values) will be investigated using a logistic regression model with covariates that include treatment arm, an indicator variable to define patients whose baseline treatment choice matched the decision analysis model, an interaction between the treatment arm and this indicator variable, as well as other clinically relevant baseline covariates and two way interactions. Testing for an interaction between treatment arm and the indicator variable will allow us to test if the effect that knowledge of the decision analytic model has on the probability of selecting the highest quality treatment decision is different for patients whose baseline treatment choice matched the decision analysis model compared to patients whose baseline treatment choice did not match the decision analysis model. Each patient will also have the following outcomes measured at 6 months, 12 months, and 24 months following their treatment: decisional regret, decisional conflict, treatment satisfaction, health state utility score, and HRQOL for urinary, bowel, and sexual function domain scores. All scores will be standardized such that each patient's score is between 0 and 100. Baseline decisional regret scores, informed decision scores, and treatment satisfaction scores will be set at 100, indicating no decisional regret, a high degree of feeling as though an informed decision was made, and high treatment satisfaction. Scores for health state utility and HRQOL domains will also be obtained at baseline. Based on each patient's final treatment choice, we will also calculate the difference between the number of quality adjusted life years based on the decision analysis model and the final treatment choice.