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

Xiaoyun Li (), Cong Chen and Wen Li
Additional contact information
Xiaoyun Li: Merck & Co., Inc.
Cong Chen: Merck & Co., Inc.
Wen Li: Merck & Co., Inc.

Statistics in Biosciences, 2018, vol. 10, issue 2, No 4, 324-341

Abstract: 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.

Keywords: Biomarker design; Oncology clinical trials; Personalized medicine; Subgroup analysis (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-016-9178-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:10:y:2018:i:2:d:10.1007_s12561-016-9178-4

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-016-9178-4

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().