Measuring the performance of prediction models to personalize treatment choice.

ABSTRACT: When data are available from individual patients receiving either a treatment or a control intervention in a randomized trial, various statistical and machine learning methods can be used to develop models for predicting future outcomes under the two conditions, and thus to predict treatment effect at the patient level. These predictions can subsequently guide personalized treatment choices. Although several methods for validating prediction models are available, little attention has been given to measuring the performance of predictions of personalized treatment effect. In this article, we propose a range of measures that can be used to this end. We start by defining two dimensions of model accuracy for treatment effects, for a single outcome: discrimination for benefit and calibration fo

SUBMITTER: Efthimiou O 

PROVIDER: S-EPMC7615726 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature