Tree-based identification of subgroups for time-varying covariate survival data

Classification and regression tree analyses identify subsets of a sample that differ on an outcome. Discrimination of subsets is performed using recursive binary splitting on a set of covariates, allowing for interactions of variable subgroups not easily captured in standard model building techniques. Using classification and regression tree with epidemiological data can be problematic as there is often a need to adjust for potential confounders and to account for time-varying covariates in the context of right-censored survival data. While classification and regression tree variations exist individually for survival analysis, time-varying covariates and incorporating possible confounders, examples of classification and regression tree using all three together are lacking. We propose a method to identify subsets of time-varying covariate risk factors that affect survival while adjusting for possible confounders. The technique is demonstrated on data from the Bypass Angioplasty Revascularization Investigation 2 Diabetes clinical trial to find combinations of modifiable time-varying cardiac risk factors (e.g. smoking status, blood pressure, lipid levels and HbA1c level) that are associated with time-to-event clinical outcomes.