Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial.

ABSTRACT:

Background

The Action for Health in Diabetes (Look AHEAD) trial investigated whether long-term cardiovascular disease morbidity and mortality could be reduced through a weight loss intervention among people with type 2 diabetes. Despite finding no significant reduction in cardiovascular events on average, it is possible that some subpopulations might have derived benefit. In this post-hoc analysis, we test the hypothesis that the overall neutral average treatment effect in the trial masked important heterogeneous treatment effects (HTEs) from intensive weight loss interventions.

Methods

We used causal forest modelling, which identifies HTEs, using a random half of the trial data (the training set). We applied Cox proportional hazards models to test the potential HTEs on the

SUBMITTER: Baum A 

PROVIDER: S-EPMC5815373 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature