A new basket trial design based on clustering of homogeneous subpopulations

Krajewska, M; Rauch, Geraldine

doi:10.1080/10543406.2021.1897993

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…We considered the cases of K = 6 and K = 10. For the case of K = 6, we set (n 20) as the Imatinib data. We evaluated the MH-RD, MH-RR with w k = 1 and MH-iwRR estimators.…”

Section: Accuracy Of the Common Parameter Estimationmentioning

confidence: 99%

“…This method was reported to have higher powers than the test simply combining all the baskets. Cunanan et al 3 and Krajewska et al 20 made some refinements. LeBlanc et al 1 and Cunanan et al 3 considered the null hypothesis of the common response rate over the baskets and took the approach to combine baskets of the seemingly similar overall response rate.…”

Section: Introductionmentioning

confidence: 99%

“…Imatinib data for advanced sarcoma; πk is the basket-specific sample proportion with the two-tailed 95% Pearson-Clopper confidence interval in the basket. Summary of the simulation design; (n 1 , n 2 , n 3 , n 4 , n 5 , n 6 ) =(7,14,8,26,10,19) for K = 6 and(n 1 , n 2 , n 3 , n 4 , n 5 , n 6 , n 7 , n 8 , n 9 , n 10 ) =(15,13,12,28,29,29,26,5,2,20) for K = 10.…”

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confidence: 99%

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Frequentist analysis of basket trials with one‐sample Mantel‐Haenszel procedures

Hattori,

Morita

2023

Statistics in Medicine

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Recent substantial advances of molecular targeted oncology drug development is requiring new paradigms for early‐phase clinical trial methodologies to enable us to evaluate efficacy of several subtypes simultaneously and efficiently. The concept of the basket trial is getting of much attention to realize this requirement borrowing information across subtypes, which are called baskets. Bayesian approach is a natural approach to this end and indeed the majority of the existing proposals relies on it. On the other hand, it required complicated modeling and may not necessarily control the type 1 error probabilities at the nominal level. In this article, we develop a purely frequentist approach for basket trials based on one‐sample Mantel‐Haenszel procedure relying on a very simple idea for borrowing information under the common treatment effect assumption over baskets. We show that the proposed Mantel‐Haenszel estimator for the treatment effect is consistent under two limiting models of the large strata and sparse data limiting models (dually consistent) and propose dually consistent variance estimators. The proposed estimators are interpretable even if the common treatment effect assumptions are violated. Then, we can design basket trials in a confirmatory matter. We also propose an information criterion approach to identify effective subclasses of baskets.

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“…We considered the cases of K = 6 and K = 10. For the case of K = 6, we set (n 20) as the Imatinib data. We evaluated the MH-RD, MH-RR with w k = 1 and MH-iwRR estimators.…”

Section: Accuracy Of the Common Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Frequentist analysis of basket trials with one‐sample Mantel‐Haenszel procedures

Hattori,

Morita

2023

Statistics in Medicine

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“…Bayesian hierarchical models were also proposed in a basket trial setting to allow information borrowing across tumor types to increase power. Berry ( 37 ) & Krajewska ( 38 ) proposed hierarchical models to cluster patient population in the basket trials. However, these models have been showed to yield inflated type I error in case of heterogeneous treatment effect ( 39 , 40 ).…”

Section: Master Protocol Designsmentioning

confidence: 99%

Challenges, opportunities, and innovative statistical designs for precision oncology trials

Yin¹,

Shen²

2022

Ann Transl Med

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In the era of precision oncology, improved understanding of tumor heterogeneity, particularly at the molecular level, has caused a shift from traditionally histology based cancer drug development to molecularly targeted drug development. The shift to the molecular view of cancer leads to increasingly small cancer populations for clinical trials which may be underpowered using traditional statistical designs. This paradigm shift lead to the recent developments of innovative clinical trial designs to address the challenges from precision oncology clinical trials. Hence, this paper reviewed and described innovative trial designs for precision oncology. Different strategies were discussed to account patient and treatment effect heterogeneity, including precision dose-finding designs that tailor the optimal dose to different patients at different time points, master protocol designs that match patients' molecular alterations with specific targeted agents, and adaptive enrichment designs that dynamically modify eligibility criteria and enroll patients that are most likely to benefit from the novel agents. Despite their superior performance, better understanding of practical barriers is needed to widen their implementation for precision oncology trials. Therefore, this paper also reviewed the practical challenges regarding the implementation of precision oncology clinical trials, along with the strength and weakness of various approaches of precision oncology clinical trial designs.

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“…Examples of frequentist approaches include the design of Cunanan et al. (2017), where all baskets are pooled only if the response rates between baskets are sufficiently similar, and the design of Krajewska and Rauch (2021), where baskets are clustered based on a

k

‐means algorithm. Most designs, however, utilize Bayesian tools since borrowing information between subgroups based on their similarity is an inherent feature of many Bayesian models.…”

Section: Introductionmentioning

confidence: 99%

Monotonicity conditions for avoiding counterintuitive decisions in basket trials

2022

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In a basket trial, a new treatment is tested in different subgroups, called the baskets. In oncology, the baskets usually comprise patients with different primary tumor sites but a common biomarker. Most basket trials are uncontrolled phase II trials and investigate a binary endpoint such as tumor response. To combine the data of baskets that show a similar response to the treatment, many basket trial designs use Bayesian borrowing methods. This increases the power compared to a basketwise analysis. However, it can lead to posterior probabilities that are not monotonically increasing in the number of responses. We show that, as a consequence, two types of counterintuitive decisions can arise—one that occurs within a single trial and one that occurs when the results are compared between different trials. We propose two monotonicity conditions for the inference in basket trials. Using a design recently proposed by Fujikawa and colleagues, we investigate the case of a single‐stage basket trial with equal sample sizes in all baskets and show that, as the number of baskets increases, these conditions are violated for a wide range of different borrowing strengths. We show that in the investigated scenarios pruning baskets can help to ensure that the monotonicity conditions hold and investigate how this affects type I error rate and power.

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A new basket trial design based on clustering of homogeneous subpopulations

Cited by 7 publications

References 18 publications

Frequentist analysis of basket trials with one‐sample Mantel‐Haenszel procedures

Frequentist analysis of basket trials with one‐sample Mantel‐Haenszel procedures

Challenges, opportunities, and innovative statistical designs for precision oncology trials

Monotonicity conditions for avoiding counterintuitive decisions in basket trials

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