To be relevant, confounding factors must 1) vary across treatment groups and 2) be expected to have an impact on patient outcomes directly. Randomized treatment assignments that yield equivalent treatment groups are said to be unconditionally “exogenous.” Thus, no factor will vary across treatment groups, and calculating effect sizes is reduced to simple comparisons of outcomes across treatment groups. However, when patients and their physicians self-select treatments, treatment groups are not expected to be equivalent. Researchers analyzing the outcomes must then identify all confounding factors (e.g., in the Leon et al. study, clinical and demographic characteristics), determine covariates from the data set to measure these confounding factors (e.g., in this case, prior symptom severity, suicidal behaviors, and comorbidities as clinical factors and socioeconomic status, marital status, age, and gender as demographic factors), and then specify an outcomes model to compute effect sizes that are adjusted for these covariates (e.g., here, a mixed-effect, grouped-time survival model).