Having determined the type of multivariable analysis to perform based on the outcome variable, one must next determine how to incorporate independent variables into the model. The important considerations are the type of independent variables you have: dichotomous, nominal, interval, or ordinal, and the relationship between the independent variable and the outcome; and the relationship between the independent variable and the outcome. Dichotomous independent variables can be used in any multivariable analysis. The other types of independent variables require special consideration. With interval variables, identifying non-linear associations is particularly important; when the association is nonlinear the variable should be transformed. The type of transformation will depend on the association with outcome. Splines are a sophisticated method of modeling complex relationships between an interval independent variable and the outcome.

When the effect of one independent variable on the dependent variable is conditional upon values of another dependent variable, we have an interactive relationship.If the effect of one variable on the dependent variable changes across various values of a second independent variable, we have an interactive relationship.This chapter provides examples of interactive relationships and how to model them using an interaction term in a linear regression.Attention is given to how to interpret interaction terms in linear regression and statistical significance for both interactions with interval level variables and dummy variables.Marginal effects graphs are illustrated to further explain interactive relationships.