Evaluating and Utilizing Surrogate Outcomes in Covariate-Adjusted Response-Adaptive Designs

2024-08-05

AI Summary

To address the challenge of balancing treatment effect heterogeneity learning and decision timeliness in adaptive clinical trials, this paper proposes a covariate-adjusted response-adaptive design that accelerates randomization probability updates using surrogate outcomes. Methodologically, it introduces the first formal framework quantifying the dual benefits—accelerated learning and bias reduction—of surrogate outcomes in sequential adaptive trials; develops an Online-Superlearner–driven mechanism for dynamic surrogate selection; and establishes a model-agnostic, targeted minimum loss–based estimation (TMLE) inference method tailored to adaptive trial data. Extensive simulations—including scenarios calibrated to real trial data—demonstrate that the design significantly improves the expected outcome for newly enrolled participants while preserving asymptotic normality of estimators. Collectively, it provides a generalizable toolkit for evaluation, selection, and inference in adaptive clinical trials.

Technology Category

Machine Learning: Online Learning & BanditsCognitive Modeling & Cognitive Systems: Adaptive BehaviorGame Theory and Economic Paradigms: Adversarial Learning

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertisingEconomics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Surrogate outcomes have long been studied as substitutes for long-term primary outcomes. However, current surrogate evaluation methods do not directly account for their benefits in updating treatment randomization probabilities in adaptive experiments that aim to learn and respond to treatment effect heterogeneity. In this context, surrogate outcomes can expedite updates to randomization probabilities and thus improve expected outcomes of newly-enrolled participants by enabling earlier detection of heterogeneous treatment effects. We introduce a novel approach for evaluating candidate surrogate outcomes that quantifies both of these benefits in sequential adaptive experiments. We also propose a new Covariate-Adjusted Response-Adaptive design that uses an Online-Superlearner to evaluate and adaptively select surrogate outcomes for updating treatment randomization probabilities during the trial. We further introduce a Targeted Maximum Likelihood Estimation method that addresses dependence in adaptively collected data and achieves asymptotic normality without parametric assumptions. Our design and estimation methods show robust performance in simulations, including those using real trial data. Overall, this framework not only provides a comprehensive way to quantify benefits and select among candidate surrogate outcomes, but also offers a general tool for evaluating various adaptive designs with inferences, providing insights into opportunities and costs of alternative designs that could have been implemented.

Problem

• Evaluating multiple candidate adaptive designs in clinical trials
• Quantifying unobserved benefits and costs of alternative designs
• Selecting optimal surrogate-guided designs for treatment effect detection

Innovation

• Meta-level adaptive design framework for real-time candidate evaluation
• Targeted Maximum Likelihood Estimation for causal estimands without parametric assumptions
• Dynamic surrogate selection to accelerate treatment effect detection