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October 2021 Inference for a two-stage enrichment design
Zhantao Lin, Nancy Flournoy, William F. Rosenberger
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Ann. Statist. 49(5): 2697-2720 (October 2021). DOI: 10.1214/21-AOS2051

Abstract

Two-stage enrichment designs can be used to target the benefiting population in clinical trials based on patients’ biomarkers. In the case of continuous biomarkers, we show that using a bivariate model that treats biomarkers as random variables more accurately identifies a treatment-benefiting enriched population than assuming biomarkers are fixed. Additionally, we show that under the bivariate model, the maximum likelihood estimators (MLEs) follow a randomly scaled mixture of normal distributions. Using random normings, we obtain asymptotically standard normal MLEs and construct hypothesis tests. Finally, in a simulation study, we demonstrate that our proposed design is more powerful than a single stage design when outcomes and biomarkers are correlated; the model-based estimators have smaller bias and mean square error (MSE) than weighted average estimators.

Acknowledgments

The authors thank an anonymous Associate Editor and referees for helpful comments that greatly improved the manuscript.

Citation

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Zhantao Lin. Nancy Flournoy. William F. Rosenberger. "Inference for a two-stage enrichment design." Ann. Statist. 49 (5) 2697 - 2720, October 2021. https://doi.org/10.1214/21-AOS2051

Information

Received: 1 May 2020; Revised: 1 January 2021; Published: October 2021
First available in Project Euclid: 12 November 2021

Digital Object Identifier: 10.1214/21-AOS2051

Subjects:
Primary: 62F12 , 62K99 , 62L05
Secondary: 60F05 , 60G52 , 62E20

Keywords: Adaptive designs , Design of experiments , Inference for stochastic processes , Precision medicine , random biomarker , threshold determination

Rights: Copyright © 2021 Institute of Mathematical Statistics

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Vol.49 • No. 5 • October 2021
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