Basket Designs: Statistical Considerations for Oncology Trials

Kaizer, Alexander; Koopmeiners, Joseph S.; Kane, Michael J.; Roychoudhury, Satrajit; Hong, David S.; Hobbs, Brian P.

doi:10.1200/po.19.00194

Cited by 30 publications

(39 citation statements)

References 49 publications

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“…While the naive pooling approach does not have a Bayesian equivalent in Tables 3 and 4, it can be noted that Kaizer et al demonstrated previously that it results in drastic inflation to the family-wise type I error rate for basket trials where the scenario is not the global null. 22 The simulation results for the trial with 10 indications are presented in the Supplementary Material (Supplementary Figures 1 and 2 for weighted marginal and family-wise type I error rates, respectively). Results with 10 indications demonstrate similar performance to trends observed for five indications.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Statistical design considerations for trials that study multiple indications

Kaizer

Koopmeiners

Chen

et al. 2020

Stat Methods Med Res

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Breakthroughs in cancer biology have defined new research programs emphasizing the development of therapies that target specific pathways in tumor cells. Innovations in clinical trial design have followed with master protocols defined by inclusive eligibility criteria and evaluations of multiple therapies and/or histologies. Consequently, characterization of subpopulation heterogeneity has become central to the formulation and selection of a study design. However, this transition to master protocols has led to challenges in identifying the optimal trial design and proper calibration of hyperparameters. We often evaluate a range of null and alternative scenarios; however, there has been little guidance on how to synthesize the potentially disparate recommendations for what may be optimal. This may lead to the selection of suboptimal designs and statistical methods that do not fully accommodate the subpopulation heterogeneity. This article proposes novel optimization criteria for calibrating and evaluating candidate statistical designs of master protocols in the presence of the potential for treatment effect heterogeneity among enrolled patient subpopulations. The framework is applied to demonstrate the statistical properties of conventional study designs when treatments offer heterogeneous benefit as well as identify optimal designs devised to monitor the potential for heterogeneity among patients with differing clinical indications using Bayesian modeling.

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Section: Simulation Resultsmentioning

confidence: 99%

“…demonstrated previously that it results in drastic inflation to the family-wise type I error rate for basket trials where the scenario is not the global null. 22…”

Section: Illustration Of Proposed Design Criteriamentioning

confidence: 99%

Statistical design considerations for trials that study multiple indications

Kaizer

Koopmeiners

Chen

et al. 2020

Stat Methods Med Res

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“…Several Bayesian solutions, developed for basket trials, offer opportunities to formalize the designs of large expansion cohorts and integrate the information across doseexpansion cohorts to facilitate evidence synthesis for planning the next phase of development. [69][70][71][72][73][74] CONCLUSIONS Classic 3+3 phase I trial designs have withstood the test of time. They offer rapid, but reasonable, dose escalation, with negligible rates of toxic death.…”

Section: Discussionmentioning

confidence: 99%

Moving Beyond 3+3: The Future of Clinical Trial Design

Kurzrock

Lin

et al. 2021

American Society of Clinical Oncology Educational Book

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Misgivings have been raised about the operating characteristics of the canonical 3+3 dose-escalation phase I clinical trial design. Yet, the traditional 3+3 design is still the most commonly used. Although it has been implied that adhering to this design is due to a stubborn reluctance to adopt change despite other designs performing better in hypothetical computer-generated simulation models, the continued adherence to 3+3 dose-escalation phase I strategies is more likely because these designs perform the best in the real world, pinpointing the correct dose and important side effects with an acceptable degree of precision. Beyond statistical simulations, there are little data to refute the supposed shortcomings ascribed to the 3+3 method. Even so, to address the unique nuances of gene- and immune-targeted compounds, a variety of inventive phase 1 trial designs have been suggested. Strategies for developing these therapies have launched first-in-human studies devised to acquire a breadth of patient data that far exceed the size of a typical phase I design and blur the distinction between dose selection and efficacy evaluation. Recent phase I trials of promising cancer therapies assessed objective tumor response and durability at various doses and schedules as well as incorporated multiple expansion cohorts spanning a variety of histology or biomarker-defined tumor subtypes, sometimes resulting in U.S. Food and Drug Administration approval after phase I. This article reviews recent innovations in phase I design from the perspective of multiple stakeholders and provides recommendations for future trials.

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“…Moreover, adjustment of multiplicity over the different subpopulations is required in planning basket trials. The manner of the adjustment of multiplicity depends on the purpose of the trial, 13,14 although there is currently no official regulatory framework 14 . Kaizer et al 13 suggested controlling the false‐positive rate in each subpopulation for exploratory settings, and overall family‐wise error rate in all subpopulation for confirmatory settings.…”

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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Basket Designs: Statistical Considerations for Oncology Trials

Cited by 30 publications

References 49 publications

Statistical design considerations for trials that study multiple indications

Statistical design considerations for trials that study multiple indications

Moving Beyond 3+3: The Future of Clinical Trial Design

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

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