For more than three decades, multivariable risk factor analysis has been the main statistical technique for identifying and quantifying treatment outcome differences adjusted for patient characteristics, these differences being treated as associations with outcomes, and not causes. There is no guarantee that risk factor analysis is an effective strategy for discovery of a cause-and-effect mechanism. Multivariable logistic regression appears to be very suitable for epi-demiological surgical research, especially for dichoto-mous outcomes such as mortality, as disease occurrence has multiple risk factors, which can be mutually correlated. The mathematical model known as a multiple logistic regression assumes that the dependent variable (the outcome of interest, such as death in this case) is linearly and additively related to the independent variables (patient risk factors) on the logistic scale. The technique is useful primarily because it produces a direct estimate of the odds ratio of any risk factor, which reflects the dose-response relationship between these and the outcome. However, if this actual relationship is non-linear, then an adequate fit can usually be achieved by adding polynomial and product interaction terms to the model, although the meaning of the odds ratios in such an interaction model would be severely circumscribed. An essential step in risk stratification modeling (RSM) is to evaluate whether there is evidence of an interaction between the variables used in a model. This interaction would imply that the effect of one of the variables is not constant over levels of the other. An important drawback of logistic regression is that it can increase bias because of misclassification and measurement errors in confounding variables and differences between conditional and unconditional odds ratio estimates of treatment effects. In this chapter, we outline all the basic components that the surgeon needs to know about risk stratification and predictive modelling.