Statistical Design and Considerations of a Phase 3 Basket Trial for Simultaneous Investigation of Multiple Tumor Types in One Study

Chen, Cong; Li, Xiaoyun; Yuan, Shuai; Antonijevic, Zoran; Kalamegham, Rasika; Beckman, Robert A.

doi:10.1080/19466315.2016.1193044

Cited by 47 publications

(90 citation statements)

References 21 publications

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“…As for the impact of the correlation ( ρ ) on the estimation bias, we observe that the bias increases and the variance decreases with ρ under both designs. According to the work of Li et al and Chen et al , min( α * ) is small when the ρ value is large. These observations suggest that, as expected, large penalty paid for type I error control (i.e.…”

Section: Intermediate Treatment Effect Associated With Maximum Biasmentioning

confidence: 98%

“…The planned number of deaths is about 165 per subpopulation, and sample size for remaining subpopulations is subject to increase to keep the overall event size fixed in the pooled population. (Because the increase of sample size diminishes the impact of “cherry‐picking”, it is associated with less type I error penalty as noticed in Chen et al .) Because PFS occurs earlier than OS, the aforementioned setup is a reasonable reflection of reality in a typical survival trial in oncology.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…No assumption is made about the ordering of treatment effect in a basket design . The design selects a subpopulation to the second stage whenever its standardized test score X i ( t ) is larger than the selection bar

Z_{1 - α_{t}}

.…”

Section: Population Selection In Absence Of Ordered Treatment Effect mentioning

confidence: 99%

“…The worked example for a basket trial follows the one in Chen et al , which is a randomized controlled trial with 1:1 randomization ratio in six tumour indications. Within the subpopulation for each tumour indication, the hypothesis testing on PFS at the interim analysis is carried out at the nominal α t level of 0.025.…”

Section: Worked Examplesmentioning

confidence: 99%

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Estimation of treatment effect in two-stage confirmatory oncology trials of personalized medicines

Chen

et al. 2017

Statist. Med.

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A personalized medicine may benefit a subpopulation with certain predictive biomarker signatures or certain disease types. However, there is great uncertainty about drug activity in a subpopulation when designing a confirmatory trial in practice, and it is logical to take a two-stage approach with the study unless credible external information is available for decision-making purpose. The first stage deselects (or prunes) non-performing subpopulations at an interim analysis, and the second stage pools the remaining subpopulations in the final analysis. The endpoints used at the two stages can be different in general. A key issue of interest is the statistical property of the test statistics and point estimate at the final analysis. Previous research has focused on type I error control and power calculation for such two-stage designs. This manuscript will investigate estimation bias of the treatment effect, which is implicit in the adjustment of nominal type I error for multiplicity control in such two-stage designs. Previous work handles the treatment effect of an intermediate endpoint as a nuisance parameter to provide the most conservative type I error control. This manuscript takes the same approach to explore the bias. The methodology is applied to the two previously studied designs. In the first design, patients with different biomarker levels are enrolled in a study, and the treatment effect is assumed to be in an order. The goal of the interim analysis is to identify a biomarker cut-off point for the subpopulations. In the second design, patients with different tumour types but the same biomarker signature are included in a trial applying a basket design. The goal of the interim analysis is to identify a subset of tumour types in the absence of treatment effect ordering. Closed-form equations are provided for the estimation bias as well as the variance under the two designs. Simulations are conducted under various scenarios to validate the analytic results that demonstrated that the bias can be properly estimated in practice. Worked examples are presented. Extensions to general adaptive designs and operational considerations are discussed. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Intermediate Treatment Effect Associated With Maximum Biasmentioning

confidence: 98%

Section: Simulation Studiesmentioning

confidence: 99%

Z_{1 - α_{t}}

.…”

Section: Population Selection In Absence Of Ordered Treatment Effect mentioning

confidence: 99%

Section: Worked Examplesmentioning

confidence: 99%

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Estimation of treatment effect in two-stage confirmatory oncology trials of personalized medicines

Chen

et al. 2017

Statist. Med.

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“…In addition, these methods are not straightforward especially when extending the two‐stage trials to more stages. Of the two‐stage designs that handle the intermediate endpoint based on the multivariate normal assumption, the ascending biomarker enrichment design and the basket design are two popular designs and have been discussed extensively in recent literature. They are straightforward and can use the intermediate endpoint at the IA for refining population.…”

Section: Introductionmentioning

confidence: 99%

Multi‐stage enrichment and basket trial designs with population selection

Zhao

et al. 2019

Statistics in Medicine

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As biomarker information from early‐phase trials can be unreliable due to high variability, it is logical to take a prospective two‐stage approach when designing a late‐phase confirmatory trial, ie, refining the target population at the first stage and performing the hypothesis testing at the second stage. The use of a reliable intermediate endpoint at the first stage can further improve the trial efficiency from both time and cost perspectives. Nevertheless, there are needs for expanding such two‐stage confirmatory designs to more stages for monitoring efficacy on the refined population. There is limited literature on this matter, particularly for two popular designs with population selection midway, ie, the biomarker enrichment design and the basket design. In this manuscript, we focus on these two popular designs and discuss how to implement the interim efficacy analyses after population refinement while controlling type I error. Power and stopping probability are also explored for the two designs.

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Categories, components, and techniques in a modular construction of basket trials for application and further research

2021

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Basket trials have become a virulent topic in medical and statistical research during the last decade. The core idea of them is to treat patients, who express the same genetic predisposition—either personally or their disease—with the same treatment irrespective of the location of the disease. The location of the disease defines each basket and the pathway of the treatment uses the common genetic predisposition among the baskets. This opens the opportunity to share information among baskets, which can consequently increase the information of the basket‐wise response with respect to the investigated treatment. This further allows dynamic decisions regarding futility and efficacy of individual baskets during the ongoing trial. Several statistical designs have been proposed on how a basket trial can be conducted and this has left an unclear situation with many options. The different designs propose different mathematical and statistical techniques, different decision rules, and also different trial purposes. This paper presents a broad overview of existing designs, categorizes them, and elaborates their similarities and differences. A uniform and consistent notation facilitates the first contact, introduction, and understanding of the statistical methodologies and techniques used in basket trials. Finally, this paper presents a modular approach for the construction of basket trials in applied medical science and forms a base for further research of basket trial designs and their techniques.

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Statistical Design and Considerations of a Phase 3 Basket Trial for Simultaneous Investigation of Multiple Tumor Types in One Study

Cited by 47 publications

References 21 publications

Estimation of treatment effect in two-stage confirmatory oncology trials of personalized medicines

Estimation of treatment effect in two-stage confirmatory oncology trials of personalized medicines

Multi‐stage enrichment and basket trial designs with population selection

Categories, components, and techniques in a modular construction of basket trials for application and further research

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