Bayesian adaptive basket trial design using model averaging

Psioda, Matthew A.; Xu, Jiawei; Jiang, Qi; Ke, Chunlei; Yang, Zhao; Ibrahim, Joseph G.

doi:10.1093/biostatistics/kxz014

Cited by 45 publications

(61 citation statements)

References 19 publications

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“…Ventz et al 227 proposed a general class of Bayesian basket trial designs using Bayesian hierarchical models and response-adaptive randomization, which facilitate both identification of a biomarker subpopulation, and identification of a subset of, for example, cancers within a biomarker-defined subpopulation that benefits from a treatment. The most recent design was proposed by Psioda et al, 228 who proposed an adaptive Bayesian basket trial design using Bayesian model averaging, which allows for some baskets to share a similar efficacy and for some to have a unique efficacy. Using simulation studies and different scenarios, the authors found that compared with basket trial designs proposed earlier, their framework achieves steady performances with respect to certain operating characteristics, such as expected sample size and different error rates.…”

Section: Basket Designs and Methodsmentioning

confidence: 99%

The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review

Meyer

Mesenbrink

Dunger-Baldauf

et al. 2020

Clinical Therapeutics

100

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Purpose: Recent years have seen a change in the way that clinical trials are being conducted. There has been a rise of designs more flexible than traditional adaptive and group sequential trials which allow the investigation of multiple substudies with possibly different objectives, interventions, and subgroups conducted within an overall trial structure, summarized by the term master protocol. This review aims to identify existing master protocol studies and summarize their characteristics. The review also identifies articles relevant to the design of master protocol trials, such as proposed trial designs and related methods. Methods: We conducted a comprehensive systematic search to review current literature on master protocol trials from a design and analysis perspective, focusing on platform trials and considering basket and umbrella trials. Articles were included regardless of statistical complexity and classified as reviews related to planned or conducted trials, trial designs, or statistical methods. The results of the literature search are reported, and some features of the identified articles are summarized. Findings: Most of the trials using master protocols were designed as single-arm (n ¼ 29/50), Phase II trials (n ¼ 32/50) in oncology (n ¼ 42/50) using a binary endpoint (n ¼ 26/50) and frequentist decision rules (n ¼ 37/50). We observed an exponential increase in publications in this domain during the last few years in both planned and conducted trials, as well as relevant methods, which we assume has not yet reached its peak. Although many operational and statistical challenges associated with such trials remain, the general consensus seems to be that master protocols provide potentially enormous advantages in efficiency and flexibility of clinical drug development. Implications: Master protocol trials and especially platform trials have the potential to revolutionize clinical drug development if the methodologic and operational challenges can be overcome.

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Section: Basket Designs and Methodsmentioning

confidence: 99%

The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review

Meyer

Mesenbrink

Dunger-Baldauf

et al. 2020

Clinical Therapeutics

100

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“…

P false[p_{i} > p_{0} false| scriptD false] > τ_{E} .

Used by: Psioda et al. (2019). As for the futility assessment one can change the reference value which then results in

P [p_{i} > \frac{p_{0} + p_{1}}{2} | D] > τ_{E} .

Used by: Berry et al.…”

Section: Analysis Of Basket Trialsmentioning

confidence: 99%

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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“…At the interim analysis with the total sample size of N total /2, if Pr [ p k ≥ 0.05| r , n ] < 0.2 (= c interim ) ( k = 1, ⋯, K ), enrollment for subpopulation ( k ) was terminated. Consistent with the simulation studies reported by Psioda et al and Simon et al 25,27 , we used the value of 0.2 for c inerim . At the final analysis, we ultimately determined treatment effectiveness in subpopulation ( k ) when Pr [ p k ≥ 0.05| r , n ] > 0.9 (= c final ) ( k = 1, ⋯, K ) was satisfied.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…Apart from these methods, recent work done by Psioda et al 25 proposed to use the Bayesian model averaging 26 to estimate the response rate of each subpopulation. The Bayesian model averaging strategy is a computer‐intensive design that averages the response rate overall possible models for evaluating a similarity in response rates among subpopulations.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian basket trial design accounting for uncertainties of homogeneity and heterogeneity of treatment effect among subpopulations

Asano

Hirakawa

2020

Pharmaceutical Statistics

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Summary Basket trials are a recent and innovative approach in oncological clinical trial design. A basket trial is a type of clinical trial for which eligibility is based on the presence of a specific genomic alteration, irrespective of cancer type. Additionally, basket trials are often used to evaluate the response rate of an investigational therapy across several types of cancer. Recently developed statistical methods for evaluating the response rate in basket trials can be generally categorized into two groups: (a) those that account for the degrees of homogeneity/heterogeneity of response rates among subpopulations, and (b) those using borrowed response rate information across subpopulations to improve the statistical efficiency using Bayesian hierarchical models. In this study, we developed a new basket trial design that accounts for the uncertainties of homogeneity and heterogeneity of response rates among subpopulations using the Bayesian model averaging approach. We demonstrated the utility of the proposed method by comparing our approach against other methods for the two methodological groups using simulated and actual data. On an average, the proposed methods offered an intermediate performance between the BHM‐weak and BHM‐strong methods. The proposed method would be useful for “signal‐finding” basket trials without prior information on the treatment effect of an investigational drug, in part because the proposed method does not require specifications regarding prior distributions of homogeneity response rates among subpopulations.

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Bayesian adaptive basket trial design using model averaging

Cited by 45 publications

References 19 publications

The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review

The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review

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

A Bayesian basket trial design accounting for uncertainties of homogeneity and heterogeneity of treatment effect among subpopulations

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