On Response-Adaptive Targeting Strategies for Multi-Treatment Experiments

2026-06-17 · Source: stat.ML updates on arXiv.org · Field: Science & Research — Mathematics & Computational Sciences, Health & Medical Research, Research Methodology & Innovation · Depth: Expert, extended

Summary

Redouane Yagouti and Emilie Kaufmann introduce "alpha"-Rebalancing Targeting Strategies ("alpha"RTS), a unified theoretical framework for response-adaptive randomization (RAR) in multi-treatment clinical trials (K ≥ 2). This framework generalizes the two-armed ERADE strategy, addressing the fragmentation in existing multi-arm RAR methods. The authors prove that all "alpha"RTS designs exhibit strong consistency, asymptotic normality for allocation proportions and treatment effect estimators, and asymptotic efficiency. To handle sparse target regimes where some treatments are asymptotically eliminated, they propose "alpha"RTS with Forced Exploration ("alpha"RTS-FE), which ensures infinite sampling for all treatments while maintaining asymptotic guarantees. Extensive simulations in 3-armed and 4-armed contexts demonstrate the finite-sample behavior of "alpha"RTS variants, including Distance-Based, ERADE 2025, and Interpolated D-Tracking, and confirm the critical role of forced exploration in sparse settings. The study also evaluates hypothesis testing performance, showing compatibility with asymptotic inference.

Key takeaway

For clinical trial designers developing multi-arm adaptive randomization strategies, you should consider the "alpha"RTS framework for its proven asymptotic efficiency and consistency. Implement "alpha"RTS with Forced Exploration when target allocations might be sparse, ensuring all treatments receive sufficient sampling. Your choice of target allocation, such as Neyman or RSIHR, can significantly impact statistical power, so select carefully over options like Tymofyeyev allocation.

Key insights

Unified "alpha"RTS framework provides asymptotically efficient, consistent multi-arm response-adaptive randomization, with forced exploration for sparse targets.

Principles

• Response-adaptive randomization improves ethical and statistical efficiency.
• All "alpha"RTS designs share strong consistency and asymptotic efficiency.
• Forced exploration is crucial for reliable adaptation in sparse target regimes.

Method

"alpha"RTS dynamically rebalances treatment allocation probabilities based on estimated target proportions, reducing over-sampled arms and increasing under-sampled ones, with optional forced exploration.

In practice

• Use "alpha" between 0.4 and 0.7 for good finite-sample performance.
• Consider Neyman or RSIHR allocations for better power in homogeneity tests.
• Implement forced exploration in multi-arm trials with potentially sparse target allocations.

Topics

• Response-Adaptive Randomization
• Multi-Treatment Experiments
• "alpha"-Rebalancing Targeting Strategies
• Forced Exploration
• Asymptotic Efficiency
• Clinical Trials

Code references

• ryagouti/rebalancing-targeting-strategies

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.