Evaluating Surrogates in Individualized Treatment Rules

TL;DR

This paper develops a framework to evaluate the effectiveness of surrogate endpoints in individualized treatment rules, especially under resource constraints, using novel performance measures and estimators.

Contribution

It introduces three ITR-oriented performance measures for surrogate endpoints, extending them to budget-constrained settings, with estimators and theoretical properties.

Findings

The proposed measures effectively evaluate surrogate endpoints in simulations.

The framework is demonstrated on the Criteo dataset, showing practical utility.

Abstract

In many decision-making problems, the primary outcome is expensive, time-consuming, or difficult to observe, so individualized treatment rules (ITRs) may be instead learned from surrogate endpoints. However, a surrogate that is highly associated with the primary outcome, or even satisfies existing surrogate criteria, may not necessarily induce a treatment rule that performs well on the primary outcome, especially under treatment resource budget constraints. In this paper, we develop a principled framework for evaluating the decision-making value of surrogate endpoints. We introduce three ITR-oriented performance measures: surrogate regret, which assesses the expected loss from using the surrogate-optimal ITR instead of outcome-optimal ITR; surrogate gain, which quantifies the benefit of surrogate-optimal ITRs relative to the no-treatment baseline; and surrogate efficiency, which evaluates improvement over random treatment assignment. We also extend them to budget-constrained settings. We propose augmented inverse probability weighted (AIPW) estimators for these measures and establish their large-sample properties. We demonstrate the proposed approach on both simulations and an application to the Criteo dataset.