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The New England Journal of Statistics in Data Science


Adaptive Sample Size Using a Totality of Evidence Approach in Rare Disease Clinical Trials
Pub. online: 5 March 2026      Type: Case Study, Application, And/or Practice Article      Open accessOpen Access
Area: Machine Learning and Data Mining
Accepted
14 February 2026
Published
5 March 2026

Abstract

Clinical trial design for rare diseases can be challenging due to limited data, heterogeneous clinical manifestations and progression, and a frequent lack of adequate knowledge about the disease. Multiple endpoints are usually used to collectively assess the effectiveness of the investigational drug on multiple aspects of the disease. Here we propose an adaptive design based on the promising zone framework, allowing for sample size re-estimation (SSR) using interim data for a clinical trial involving multiple endpoints. The proposed SSR procedure incorporates two global tests: the ordinary least squares (OLS) test and the nonparametric permutation test. We consider two SSR approaches: one is based on power (SSR-Power) and the other on conditional power (SSR-CP). Simulation results show that the adaptive design achieves type I error control and satisfactory power. Compared with the permutation test, the OLS test has improved type I error control when the sample size is small and the timing of the interim analysis is early; while the permutation test achieves slightly higher power in most scenarios. Regarding the SSR methods, SSR-CP consistently achieves higher power than SSR-Power but often requires a larger sample size and more frequently reaches the maximum allowable sample size. The proposed design is particularly useful when the trial has a small initial sample size and has opportunity to adjust the sample size at an interim analysis to achieve adequate power.

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© 2026 New England Statistical Society
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Keywords
Adaptive design Clinical trials Cohen’s d Conditional power Global test Group sequential design Ordinary least squares Permutation Promising zone Rare disease Sample size re-estimation Small population Totality of evidence

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