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

TL;DR

This paper introduces a threshold-free index to measure how well covariates can identify individuals who will benefit most from treatment, improving upon traditional subgroup analysis methods.

Contribution

It proposes a novel, threshold-free metric for quantifying covariate capacity in treatment benefit prediction, with a semi-parametric estimation approach and practical demonstration.

Findings

The index can be expressed as an integrated treatment benefit over covariates.

The metric has an intuitive interpretation and can be estimated with common regression models.

Application to clinical trial data illustrates its practical utility.

Abstract

The focus of this paper is on quantifying the capacity of covariates in devising efficient treatment rules when data from a randomized trial are available. Conventional one-variable-at-a-time subgroup analysis based on 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 at the reporting stages of a clinical trial. We propose a threshold-free metric that quantifies the capacity of a set of covariates towards finding individuals who will benefit the most from treatment. The construct of the proposed metric is comparing the expected outcomes with and without knowledge of covariates when one of a two randomly selected patients are to be treated. We show that the resulting index can also be expressed in terms of integrated treatment benefit as a function of covariates over the entire range of treatment thresholds. We also propose a semi-parametric estimation method suitable for out-of-sample validation and adjustment for optimism. We use data from a clinical trial of preventive antibiotic therapy for reducing exacerbation rate in Chronic Obstructive Pulmonary Disease 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 presented in this work, various aspects of estimation and inference for such metrics need to be pursued in future research.