The advantage of tree-based models over linear and additive models is that they are more adept at capturing non-additive relationships and more easily uncover complex interactions between predictor variables. Continuous variables or categorical variables with more than two levels need not be dichotomized prior to the analysis - the tree algorithm determines the cutoff value that produces the most homogenous nodes. Missing data can be treated as a separate category. Initial tree models will include risk factors listed in the following table as well as demographic variables, age, sex, and race.