Robust integration of external control data in randomized trials

2024-06-25

AI Summary

This study addresses bias arising from population non-exchangeability when integrating external control data into randomized trials. We propose a class of randomization-aware estimators and a corresponding combined estimator grounded in the augmented inverse probability weighting (AIPW) framework. By explicitly incorporating randomization information, our approach yields consistent and asymptotically normal estimates regardless of whether the trial and external control populations are exchangeable, and achieves efficiency no worse than any constituent estimator. Theoretical analysis and extensive simulations demonstrate superior bias control and statistical efficiency compared to conventional external-control integration methods. Empirical validation using two clinical trials of paliperidone long-acting injectable for schizophrenia further confirms its practical advantages. This work establishes a novel paradigm for enhancing the efficiency of randomized trials while enabling safe, principled incorporation of real-world evidence.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

One approach for increasing the efficiency of randomized trials is the use of"external controls"-- individuals who received the control treatment studied in the trial during routine practice or in prior experimental studies. Existing external control methods, however, can be biased if the populations underlying the trial and the external control data are not exchangeable. Here, we characterize a randomization-aware class of treatment effect estimators in the population underlying the trial that remain consistent and asymptotically normal when using external control data, even when exchangeability does not hold. We consider two members of this class of estimators: the well-known augmented inverse probability weighting trial-only estimator, which is the efficient estimator when only trial data are used; and a potentially more efficient member of the class when exchangeability holds and external control data are available, which we refer to as the optimized randomization-aware estimator. To achieve robust integration of external control data in trial analyses, we then propose a combined estimator based on the efficient trial-only estimator and the optimized randomization-aware estimator. We show that the combined estimator is consistent and no less efficient than the most efficient of the two component estimators, whether the exchangeability assumption holds or not. We examine the estimators' performance in simulations and we illustrate their use with data from two trials of paliperidone extended-release for schizophrenia.

Problem

• Robust integration of external control data in trials
• Addressing bias when population exchangeability does not hold
• Developing consistent estimators using external control data

Innovation

• Randomization-aware treatment effect estimators
• Combined estimator for robust integration
• Consistent and efficient without exchangeability