Performance metrics for models designed to predict treatment effect.

ABSTRACT:

Background

Measuring the performance of models that predict individualized treatment effect is challenging because the outcomes of two alternative treatments are inherently unobservable in one patient. The C-for-benefit was proposed to measure discriminative ability. However, measures of calibration and overall performance are still lacking. We aimed to propose metrics of calibration and overall performance for models predicting treatment effect in randomized clinical trials (RCTs).

Methods

Similar to the previously proposed C-for-benefit, we defined observed pairwise treatment effect as the difference between outcomes in pairs of matched patients with different treatment assignment. We match each untreated patient with the nearest treated patient based on the Mahalanobis di

SUBMITTER: Maas CCHM 

PROVIDER: S-EPMC10329397 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature