Subgroup identification for differential cardio-respiratory fitness effect on cardiovascular disease risk factors: A model-based recursive partitioning approach

The goal of this study is twofold: i) identification of features associated with three cardiovascular disease (CVD) risk factors, and (ii) identification of subgroups with differential treatment effects. Multivariate analysis is performed to identify the features associated with the CVD risk factors: hypertension, diabetes, and dyslipidemia. For subgroup identification, we applied model-based recursive partitioning approach. This method fits a local model in each subgroup of the population rather than fitting one global model for the whole population. The method starts with a model for the overall effect of treatment and checks whether this effect is equally applicable for all individuals under the study based on parameter instability of M fluctuation test over a set of partitioning variables. The procedure produces a segmented model with a differential effect of cardio-respiratory fitness (CRF) corresponding to each subgroup. The subgroups are linked to predictive factors learned by the recursive partitioning approach. This approach is applied to the data from the Ball State Adult Fitness Program Longitudinal Lifestyle Study (BALL ST), where we considered the level of CRF as a treatment variable. The overall results indicate that CRF is inversely associated with hypertension, diabetes and dyslipidemia. The partitioning factors that are selected are related to these risk factors. The subgroup-specific results indicate that for each subgroup, the chance of hypertension, diabetes and dyslipidemia increases with low CRF.