The Experimental Unit Information Index: Balancing Evidentiary Value and Sample Size of Adaptive Designs

2025-11-21

AI Summary

This study addresses the critical challenge of reducing the number of experimental units in animal studies—aligning with the “Reduce” principle of the 3Rs—while preserving statistical inference reliability. We propose the Experimental Unit Information Index (EUII), a novel metric that quantifies the evidential contribution of a single experimental unit under a given design. EUII unifies frequentist criteria (Type I error rate, statistical power) and Bayesian measures (posterior probability), and naturally extends to adaptive designs permitting early stopping. Theoretically, its asymptotic value depends solely on the standardized effect size under the alternative hypothesis. Through simulations and applications involving group-sequential and adaptive testing methods, we demonstrate that EUII enables dynamic sample-size re-estimation: it maintains strict error-rate control while substantially improving per-unit information efficiency. Thus, EUII establishes a new paradigm for ethically constrained, statistically efficient animal experiment design.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Reducing the number of experimental units is one of the three pillars of the 3R principles (Replace, Reduce, Refine) in animal research. At the same time, statistical error rates need to be controlled to enable reliable inferences and decisions. This paper proposes a novel measure to quantify the evidentiary value of one experimental unit for a given study design. The experimental unit information index (EUII) is based on power, Type-I error and sample size, and has attractive interpretations both in terms of frequentist error rates and Bayesian posterior odds. We introduce the EUII in simple statistical test settings and show that its asymptotic value depends only on the assumed relative effect size under the alternative. We then extend the definition to adaptive designs where early stopping for efficacy or futility may cause reductions in sample size. Applications to group-sequential designs and a recently proposed adaptive statistical test procedure show the usefulness of the approach when the goal is to maximize the evidentiary value of one experimental unit.

Problem

• Quantifying evidentiary value per experimental unit in study designs
• Balancing statistical power and sample size in adaptive designs
• Maximizing information from experimental units while controlling error rates

Innovation

• Proposes experimental unit information index measure
• Quantifies evidentiary value using power and error rates
• Extends approach to adaptive designs with early stopping