Using the demographic, clinical, imaging, biomarker and genetic measures, plus any interactions, identified in the tree analysis, Cox proportional hazards regression will be used to develop a predictive model from which a summary risk score can be calculated and applied in clinical settings. Parametric regression methods may be used if the proportional hazards assumption is not met. The regression model will be developed using a 100-fold validation procedure whereby 50% of the observations will be randomly selected for model development (test sample) and the remaining 50% used to assess the model's predictive ability (validation sample). This procedure will be repeated 100 times with a different random selection of test-validation samples for each iteration. The c-index will be calculated to assess model discrimination for both test and validation samples30. The ensemble of test-validation models provides an estimate of the attenuation of the c-index when the model is applied to a different sample as well as consistency of specific risk factors appearing in the models. A final model will be constructed that includes the combination of the strongest (high c-index and frequency of appearance) predictors. This model will then be applied to the entire sample to estimate regression coefficients and hazard ratios and calculate a summary risk score30. Nomograms will be constructed from the final model to provide predicted probabilities of the risk scores. To increase the ease of use of a nomogram, variables which are statistically significant but have little effect on prediction may be excluded. The model will also be used to develop a prediction algorithm that rapidly calculates risk on a hand-held electronic device. This analysis may also inform the design of clinical trials to evaluate therapeutic efficacy by providing effect sizes (hazard ratios) to calculate sample size requirements.