A Bayesian Method for the Detection of Proof of Concept in Early Phase Oncology Studies with a Basket Design

Jin Jin (), Qianying Liu, Wei Zheng, Zhenming Shun, Tun Tun Lin, Lei Gao and Yingwen Dong
Additional contact information
Jin Jin: Johns Hopkins Bloomberg School of Public Health
Qianying Liu: Sanofi
Wei Zheng: Kehang Info and Tech Ltd
Zhenming Shun: Daiichi Sankyo
Tun Tun Lin: Sanofi
Lei Gao: Vertex Pharmaceuticals
Yingwen Dong: Sanofi

Statistics in Biosciences, 2020, vol. 12, issue 2, No 6, 167-179

Abstract: Abstract In the clinical drug development, proof of clinical concept (PoC) refers to the evidence of treatment efficacy that is obtained from early phase clinical studies. PoC is critical, as it motivates the initiation of late stage clinical trials, and has a profound impact on the “Chemistry, Manufacturing and Controls” (CMC) process, which is preferably launched as early as possible so as to save valuable time for drug development. A new type of oncology clinical trial called basket trial has emerged recently, where the experimental treatment targets on a specific oncogenic pathway that is hypothesized to modulate tumor growth and/or metastasis, and patients with potentially multiple cancer types can be enrolled. The problem of PoC in basket trials has not been formally investigated in the statistical literature. In early phase basket trials, the commonly used independent analysis lacks statistical power of detecting PoC due to limited sample size. A more powerful approach is needed, especially when the treatment effect is not strong enough for each individual cancer type. In this paper, we propose a novel approach for PoC detection in the early phase basket trials under a Bayesian framework. We classify cancer types into a “sensitive subgroup” that responds positively to the treatment, and an “insensitive subgroup” that does not respond to the treatment. We then assess PoC using the posterior probability that at least one cancer type is sensitive. Simulation results show that our proposed approach has a promising performance, with considerable gain in power compared with the independent approach when a relatively large number of the cancer types are sensitive to the treatment.

Keywords: Basket trials; Early phase oncology studies; Bayesian hierarchical models; Proof of clinical concept; Information borrowing across cancer types (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s12561-020-09267-2 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:12:y:2020:i:2:d:10.1007_s12561-020-09267-2

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

DOI: 10.1007/s12561-020-09267-2

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 ().