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The focus of this paper is on quantifying the capacity of covariates indevising efficient treatment rules when data from a randomized trial areavailable. Conventional one-variable-at-a-time subgroup analysis based onstatistical hypothesis testing of covariate-by-treatment interaction isill-suited for this purpose. The application of decision theory results intreatment rules that compare the expected benefit of treatment given thepatient's covariates against a treatment threshold. However, determiningtreatment threshold is often context-specific, and any given threshold mightseem arbitrary at the reporting stages of a clinical trial. We propose athreshold-free metric that quantifies the capacity of a set of covariatestowards finding individuals who will benefit the most from treatment. Theconstruct of the proposed metric is comparing the expected outcomes with andwithout knowledge of covariates when one of a two randomly selected patientsare to be treated. We show that the resulting index can also be expressed interms of integrated treatment benefit as a function of covariates over theentire range of treatment thresholds. We also propose a semi-parametricestimation method suitable for out-of-sample validation and adjustment foroptimism. We use data from a clinical trial of preventive antibiotic therapyfor reducing exacerbation rate in Chronic Obstructive Pulmonary Disease todemonstrate the calculations in a step-by-step fashion. The proposed index hasintuitive and theoretically sound interpretation and can be estimated withrelative ease for a wide class of regression models. Beyond the conceptualdevelopments presented in this work, various aspects of estimation andinference for such metrics need to be pursued in future research.

A threshold‐free summary index for quantifying the capacity of covariates to yield efficient treatment rules