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Adaptive Biomarker Population Selection in Phase III Confirmatory Trials with Time-to-Event Endpoints

Author

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  • Xiaoyun Li

    (Merck & Co., Inc.)

  • Cong Chen

    (Merck & Co., Inc.)

  • Wen Li

    (Merck & Co., Inc.)

Abstract

A key component of modern drug development is to identify the patient population(s) that benefit most from the therapy. Subjects with different biomarker/genetic profiles may respond to a therapy differently. At the same time, data of populations with best efficacy may not be available due to lack of randomized data or biomarker assay when designing the phase III confirmatory trials. In this manuscript, we propose an adaptive design that performs biomarker-informed population selection at the interim analysis, so as to refine the population for primary hypothesis at the final analysis. Unlike most of the previous research work, where the same endpoint is used for interim population selection and final analysis, we propose to use a sensitive intermediate endpoint (whenever available) for population selection. Treatment effect of the intermediate endpoint is treated as a nuisance parameter to ensure type I error control. The use of a sensitive intermediate endpoint for biomarker population selection further improves study power. Simulations were conducted to evaluate the control of overall type I error, probabilities of population selection, and power.

Suggested Citation

  • Xiaoyun Li & Cong Chen & Wen Li, 2018. "Adaptive Biomarker Population Selection in Phase III Confirmatory Trials with Time-to-Event Endpoints," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 324-341, August.
    • Handle: RePEc:spr:stabio:v:10:y:2018:i:2:d:10.1007_s12561-016-9178-4
    DOI: 10.1007/s12561-016-9178-4
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    References listed on IDEAS

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    1. Tomasz Burzykowski & Geert Molenberghs & Marc Buyse & Helena Geys & Didier Renard, 2001. "Validation of surrogate end points in multiple randomized clinical trials with failure time end points," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 405-422.
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