Bridging the gap between experimental burden and statistical power for quantiles equivalence testing

TL;DR

This paper introduces an improved statistical method, alpha-qTOST, for more powerful and accurate quantile equivalence testing in heterogeneous Gaussian samples, especially for extreme quantiles and small, unbalanced samples.

Contribution

The paper develops alpha-qTOST, a finite-sample adjustment to the existing qTOST method, enhancing power while maintaining size, and extends the framework to multiple quantiles with theoretical and empirical validation.

Findings

alpha-qTOST outperforms qTOST in power, especially for extreme quantiles.

The method maintains correct test size at the nominal level.

Case studies demonstrate practical advantages in clinical and experimental settings.

Abstract

Testing the equivalence of multiple quantiles between two populations is important in many scientific applications, such as clinical trials, where conventional mean-based methods may be inadequate. This is particularly relevant in bridging studies that compare drug responses across different experimental conditions or patient populations. These studies often aim to assess whether a proposed dose for a target population achieves pharmacokinetic levels comparable to those of a reference population where efficacy and safety have been established. The focus is on extreme quantiles which directly inform both efficacy and safety assessments. When analyzing heterogeneous Gaussian samples, where a single quantile of interest is estimated, the existing Two One-Sided Tests method for quantile equivalence testing (qTOST) tends to be overly conservative. To mitigate this behavior, we introduce $\alpha$-qTOST, a finite-sample adjustment that achieves uniformly higher power compared to qTOST while maintaining the test size at the nominal level. Moreover, we extend the quantile equivalence framework to simultaneously assess equivalence across multiple quantiles. Through theoretical guarantees and an extensive simulation study, we demonstrate that $\alpha$-qTOST offers substantial improvements, especially when testing extreme quantiles under heteroskedasticity and with small, unbalanced sample sizes. We illustrate these advantages through two case studies, one in HIV drug development, where a bridging clinical trial examines exposure distributions between male and female populations with unbalanced sample sizes, and another in assessing the reproducibility of an identical experimental protocol performed by different operators for generating biodistribution profiles of topically administered and locally acting products.