Clinical Value of Predicting Individual Treatment Effects for Intensive Blood Pressure Therapy.

ABSTRACT:

Background

The absolute risk reduction (ARR) in cardiovascular events from therapy is generally assumed to be proportional to baseline risk-such that high-risk patients benefit most. Yet newer analyses have proposed using randomized trial data to develop models that estimate individual treatment effects. We tested 2 hypotheses: first, that models of individual treatment effects would reveal that benefit from intensive blood pressure therapy is proportional to baseline risk; and second, that a machine learning approach designed to predict heterogeneous treatment effects-the X-learner meta-algorithm-is equivalent to a conventional logistic regression approach.

Methods and results

We compared conventional logistic regression to the X-learner approach for prediction of 3-year ca

SUBMITTER: Duan T 

PROVIDER: S-EPMC6756170 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature