Fig. 1

Random Forests for Survival, Longitudinal, and Multivariate (RF-SLAM) Data Analysis Overview. The Random Forests for Survival, Longitudinal, and Multivariate (RF-SLAM) data analysis approach begins with a pre-processing step to create counting process information units (CPIUs) within which we can model the possibly multivariate outcomes of interest (e.g. SCA, HF) and accommodate time-dependent covariates. For the LV Structural Predictors Registry, the time-varying covariates of interest relate to heart failure hospitalizations (HFs), indicated by the blue diamonds. In this case, CPIUs are created from the Survival, Longitudinal, and Multivariate (SLAM) data by creating a new CPIU every half year, corresponding to the frequency of follow up. The variable int.n represents the interval number indicating time since study enrollment in half-years. The time-varying covariates are int.n and pHF (total number of previous heart failure hospitalizations since study enrollment). Then, these CPIUs (containing the time-varying covariates along with the baseline predictors) are used as inputs in the RF-SLAM algorithm to generate the predicted probability of an SCA. The SCA event indicator is denoted with iSCA (0 if no event within CPIU, 1 if the event occurs within CPIU) and the heart failure hospitalization event indicator is iHF (0 if no event within CPIU, 1 if the event occurs within CPIU)