Decision-making around treatment for non-metastatic prostate cancer is complex, with radical treatment associated with significant potential morbidity despite some tumours being relatively indolent. Prognostic stratification should therefore help inform management. However, models currently used in clinical practice have significant flaws. This thesis sets out the rationale for a new individualised prognostic model, describes the development and validation of a novel model called Predict Prostate, and then evaluates the impact of this model in clinical practice. Chapter 1 introduces the topic and includes a systematic review of existing tools, including a collaborative screening process of 6,597 records. Very few individualised prognostic tools were identified, and those found were considered inadequate by failing to include treatment effect, and disregarding non-cancer mortality. Chapter 2 describes the development of an algorithm to estimate individualised 15-year prostate cancer-specific, non-prostate cancer, and overall mortality. Data on 10,089 men from the UK National Cancer Registration and Analysis Service were split into model-development and validation cohorts. An additional validation was performed in a small international cohort. Chapter 3 describes the updating and large external validation of the model in a geographically independent cohort of 69,206 men from the Swedish Prostate Cancer database. Overall the model was well calibrated, and discriminatory performance of the model generally exceeded existing models. Chapters 4 and 5 evaluate the application of the model. First, model estimates were presented to health care professionals in a randomised online format using hypothetical clinical vignettes. Clinicians were found to overestimate cancer lethality, and were less likely to recommend radical treatment when shown Predict Prostate estimates. Chapter 5 describes the multi-centre randomised controlled trial assessing the impact of the model among newly diagnosed prostate cancer patients. Here, the model was shown to shift perceptions around prognosis, reduce decisional conflict and uncertainty, and was popular with patients. Chapter 6 summates and concludes the thesis.