An Efficient Adaptive Sequential Procedure for Simple Hypotheses with Expression for Finite Number of Applications of Less Effective Treatment

TL;DR

This paper introduces an adaptive sequential testing procedure that minimizes exposure to less effective treatments while maintaining efficiency and accuracy, with explicit formulas and simulation validation.

Contribution

It provides a novel adaptive framework with a closed-form expression for expected inferior treatment applications, balancing statistical efficiency and ethical considerations.

Findings

Achieves finite exposure to inferior treatment.

Maintains asymptotic efficiency similar to SPRT.

Reduces inferior allocations significantly.

Abstract

We propose an adaptive sequential framework for testing two simple hypotheses that analytically ensures finite exposure to the less effective treatment. Our proposed procedure employs a likelihood ratio-driven adaptive allocation rule, dynamically concentrating sampling effort on the superior population while preserving asymptotic efficiency (in terms of average sample number) comparable to the Sequential Probability Ratio Test (SPRT). The foremost contribution of this work is the derivation of an explicit closed-form expression for the expected number of applications to the inferior treatment. This approach achieves a balanced method between statistical precision and ethical responsibility, aligning inferential reliability with patient safety. Extensive simulation studies substantiate the theoretical results, confirming stability in allocation and consistently high probability of correct selection (PCS) across different settings. In addition, we demonstrate how the adaptive procedure markedly reduces inferior allocations compared with the classical SPRT, highlighting its practical advantage in ethically sensitive sequential testing scenarios. The proposed design thus offers an ethically efficient and computationally tractable framework for adaptive sequential decision-making.