Abstract

Adaptive enrichment design plays a crucial role in clinical trials where

treatment effects vary across subgroups. However, previous works often focus on

subgroups determined by a single biomarker. In this paper, we extend the framework to allow subgroups to be defined by multiple biomarkers. We introduce a

flexible subgroup modeling framework and employ variable selection methods to

identify predictive and prognostic biomarkers simultaneously. Simulation studies demonstrate that the proposed adaptive enrichment design yields accurate

subgroup-specific estimates, valid p-values, and achieves the desired statistical

power via appropriate Stage 2 sample size determination. Finally, we apply the

adaptive enrichment design to the National Supported Work dataset for empirical

illustration.

Key words and phrases: Adaptive enrichment design, variable selection, subgroup selection and analysis, sample size determination

Information

Preprint No.SS-2025-0498
Manuscript IDSS-2025-0498
Complete AuthorsMingde Wu, Juan Shen, Ruqian Zhang, Emma Jingfei Zhang
Corresponding AuthorsJuan Shen
Emailsshenjuan@fudan.edu.cn

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Acknowledgments

This work was partially supported by the National Nature and Science

Foundation of China (12331009).

Supplementary Materials

The online Supplementary Material contains the derivation of the subgroup

ATE estimators and variance expressions, approximate null distributions of

the test statistics, additional numerical results and real data results.

Supplementary materials are available for download.