THE UNIVERSITY OF BRITISH COLUMBIA
When data on treatment assignment, outcomes, and covariates from a randomized trial are available, a question of interest is to what extent covariates can be used to optimize treatment decisions. Statistical hypothesis testing of covariate-by-treatment interaction is ill-suited for this purpose. The application of decision theory results in treatment rules that compare the expected benefit of treatment given the patient's covariates against a treatment threshold. However, determining treatment threshold is often context-specific, and any given threshold might seem arbitrary when the overall capacity towards predicting treatment benefit is of concern. We propose the Concentration of Benefit index (Cb ), a threshold-free metric that quantifies the combined performance of covariates towards finding individuals who will benefit the most from treatment. The construct of the proposed index is comparing expected treatment outcomes with and without knowledge of covariates when one of a two randomly selected patients is to be treated. We show that the resulting index can also be expressed in terms of the integrated efficiency of individualized treatment decision over the entire range of treatment thresholds. We propose parametric and semiparametric estimators, the latter being suitable for out-of-sample validation and correction for optimism. We used data from a clinical trial to demonstrate the calculations in a step-by-step fashion. The proposed index has intuitive and theoretically sound interpretation and can be estimated with relative ease for a wide class of regression models. Beyond the conceptual developments, various aspects of estimation and inference for such a metric need to be pursued in future research. R code that implements the method for a variety of regression models is provided at (https://github.com/msadatsafavi/txBenefit).