Bayesian sample size determination in basket trials borrowing information between subsets.

Basket trials are increasingly used for the simultaneous evaluation of a new treatment in various patient subgroups under one overarching protocol. We propose a Bayesian approach to sample size determination in basket trials that permit borrowing of information between commensurate subsets. Specifically, we consider a randomized basket trial design where patients are randomly assigned to the new treatment or control within each trial subset ("subtrial" for short). Closed-form sample size formulae are derived to ensure that each subtrial has a specified chance of correctly deciding whether the new treatment is superior to or not better than the control by some clinically relevant difference. Given prespecified levels of pairwise (in)commensurability, the subtrial sample sizes are solved simultaneously. The proposed Bayesian approach resembles the frequentist formulation of the problem in yielding comparable sample sizes for circumstances of no borrowing. When borrowing is enabled between commensurate subtrials, a considerably smaller trial sample size is required compared to the widely implemented approach of no borrowing. We illustrate the use of our sample size formulae with two examples based on real basket trials. A comprehensive simulation study further shows that the proposed methodology can maintain the true positive and false positive rates at desired levels.

A Bayesian platform trial design to simultaneously evaluate multiple drugs in multiple indications with mixed endpoints.

In the era of targeted therapies and immunotherapies, the traditional drug development paradigm of testing one drug at a time in one indication has become increasingly inefficient. Motivated by a real-world application, we propose a master-protocol-based Bayesian platform trial design with mixed endpoints (PDME) to simultaneously evaluate multiple drugs in multiple indications, where different subsets of efficacy measures (eg, objective response and landmark progression-free survival) may be used by different indications as single or multiple endpoints. We propose a Bayesian hierarchical model to accommodate mixed endpoints and reflect the trial structure of indications that are nested within treatments. We develop a two-stage approach that first clusters the indications into homogeneous subgroups and then applies the Bayesian hierarchical model to each subgroup to achieve precision information borrowing. Patients are enrolled in a group-sequential way and adaptively assigned to treatments according to their efficacy estimates. At each interim analysis, the posterior probabilities that the treatment effect exceeds prespecified clinically relevant thresholds are used to drop ineffective treatments and "graduate" effective treatments. Simulations show that the PDME design has desirable operating characteristics compared to existing method.

A comparison of Bayesian information borrowing methods in basket trials and a novel proposal of modified exchangeability-nonexchangeability method.

Recent innovation in trial design to improve study efficiency has led to the development of basket trials in which a single therapeutic treatment is tested on several patient populations, each of which forms a basket. In a common setting, patients across all baskets share a genetic marker and as such, an assumption can be made that all patients may have a homogeneous response to treatments. Bayesian information borrowing procedures utilize this assumption to draw on information regarding the response in one basket when estimating the response rate in others. This can improve power and precision of estimates particularly in the presence of small sample sizes, however, can come at a cost of biased estimates and an inflation of error rates, bringing into question validity of trial conclusions. We review and compare the performance of several Bayesian borrowing methods, namely: the Bayesian hierarchical model (BHM), calibrated Bayesian hierarchical model (CBHM), exchangeability-nonexchangeability (EXNEX) model and a Bayesian model averaging procedure. A generalization of the CBHM is made to account for unequal sample sizes across baskets. We also propose a modification of the EXNEX model that allows for better control of a type I error. The proposed method uses a data-driven approach to account for the homogeneity of the response data, measured through Hellinger distances. Through an extensive simulation study motivated by a real basket trial, for both equal and unequal sample sizes across baskets, we show that in the presence of a basket with a heterogeneous response, unlike the other methods discussed, this model can control type I error rates to a nominal level whilst yielding improved power.

[Research progress on genome-guided precision oncology and development ideas of antitumor Chinese medicine].

Genome-guided oncology refers to a new treatment concept that transcends histological classification and pathological ty-ping and uses drugs according to the genetic characteristics of tumors. New drug development technology and clinical trial design based on this concept provide new ideas for the clinical application of precision oncology. The multi-component and multi-target characteristics of Chinese medicine provide rich resources for the development of tumor-targeting drugs from natural products, and the design of the master protocol trial aiming at the characteristics of precision oncology supports the rapid clinical screening of effective tumor-targeting drugs. The emergence of the synthetic lethality strategy breaks through the bottleneck that the drug can only target the oncogene but cannot do anything to the tumor suppressor gene with the loss-of-function mutation in the past. With the rapid development of high-throughput sequencing technology, the cost of sequencing is also decreasing. For the development of tumor-targeting drugs, how to keep up with the update speed of target information is a difficult problem of concern. Based on the integration of innovative ideas and me-thods of precision oncology, network pharmacology, and synthetic lethality strategy on synthetic lethal interaction network of antitumor Chinese medicine compatibility formula design, and the combination of improvement of innovative clinical trial methods, such as master protocol trial, basket trial, and umbrella trial, unique advantages of Chinese medicine are expected to be exerted beyond the antibody-based drugs and small molecule-based drugs and corresponding targeted drugs are potentially developed for clinical application.

Bayesian local exchangeability design for phase II basket trials.

We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). In our proposed local-MEM framework, information borrowing is only allowed to occur locally, that is, among baskets with similar response rate and the amount of information borrowing is determined by the level of similarity in response rate, whereas baskets not considered similar are not allowed to share information. We construct a two-stage design for phase II basket trials using the proposed strategy. The proposed method is compared to competing Bayesian methods and Simon's two-stage design in a variety of simulation scenarios. We demonstrate the proposed method is able to maintain the family-wise type I error rate at a reasonable level and has desirable basket-wise power compared to Simon's two-stage design. In addition, our method is computationally efficient compared to existing Bayesian methods in that the posterior profiles of interest can be derived explicitly without the need for sampling algorithms. R scripts to implement the proposed method are available at https://github.com/yilinyl/Bayesian-localMEM.

Mobilizing the clinical trial ecosystem to drive adoption of master protocols.

Master protocol studies typically use an overarching protocol to answer several questions by guiding a variety of sub-studies. These sub-studies can incorporate multiple diseases, therapies, or both. Although this innovative approach offers many benefits, including the ability to deliver clinical research that is more patient-centric and efficient, several common barriers curtail widespread adoption. The Clinical Trials Transformation Initiative (CTTI) convened industry representatives, regulatory agencies, patient groups, and academic institutions to identify emerging best practices and develop resources designed to help sponsors and other stakeholders overcome these challenges. We first identify some broad changes needed in the clinical trials ecosystem to facilitate mainstream adoption of master protocol studies, and we subsequently summarize CTTI's resources designed to support this effort.

PrecISE: Precision Medicine in Severe Asthma: An adaptive platform trial with biomarker ascertainment.

Severe asthma accounts for almost half the cost associated with asthma. Severe asthma is driven by heterogeneous molecular mechanisms. Conventional clinical trial design often lacks the power and efficiency to target subgroups with specific pathobiological mechanisms. Furthermore, the validation and approval of new asthma therapies is a lengthy process. A large proportion of that time is taken by clinical trials to validate asthma interventions. The National Institutes of Health Precision Medicine in Severe and/or Exacerbation Prone Asthma (PrecISE) program was established with the goal of designing and executing a trial that uses adaptive design techniques to rapidly evaluate novel interventions in biomarker-defined subgroups of severe asthma, while seeking to refine these biomarker subgroups, and to identify early markers of response to therapy. The novel trial design is an adaptive platform trial conducted under a single master protocol that incorporates precision medicine components. Furthermore, it includes innovative applications of futility analysis, cross-over design with use of shared placebo groups, and early futility analysis to permit more rapid identification of effective interventions. The development and rationale behind the study design are described. The interventions chosen for the initial investigation and the criteria used to identify these interventions are enumerated. The biomarker-based adaptive design and analytic scheme are detailed as well as special considerations involved in the final trial design.

Hierarchical Bayesian clustering design of multiple biomarker subgroups (HCOMBS).

Given the Food and Drug Administration's (FDA's) acceptance of master protocol designs in recent guidance documents, the oncology field is rapidly moving to address the paradigm shift to molecular subtype focused studies. Identifying new "marker-based" treatments requires new methodologies to address the growing demand to conduct clinical trials in smaller molecular subpopulations, identify effective treatment and marker interactions, and control for false positives. We introduce our methodology, Hierarchical Bayesian Clustering Design of Multiple Biomarker Subgroups (HCOMBS), a two-stage umbrella Phase II design with effect size clustering and information borrowing across multiple biomarker-treatment pairs. HCOMBS was designed to reduce required sample size, differentiate between varying effect sizes, and control for operating characteristics in the multi-arm setting. When compared to independently applied Simon's Optimal two-stage design, we showed through simulations that HCOMBS required less participants per treatment arm with a well-controlled family-wise error rate and desirable marginal power. Additionally, HCOMBS features a statistical approach that simultaneously conducts clustering and hypothesis testing in one step. We also applied the proposed design on the alliance brain metastases umbrella trial.

Optimal one-stage design and analysis for efficacy expansion in Phase I oncology trials.

BACKGROUND: Contemporary Phase I oncology trials often include efficacy expansion in various tumor indications post dose finding. Preliminary anti-tumor activity from efficacy expansion can aid Go/No-Go decision for Phase 2 or Phase 3 initiation. Tumor cohorts in efficacy expansion are commonly analyzed independently in practice, which are often underpowered due to small sample size. Pooled analysis is also sometimes conducted, but it ignores the heterogeneity of the anti-tumor activity across cohorts. METHODS: We propose an optimal one-stage design and analysis strategy for the efficacy expansion to assess whether the treatment is effective. Allowing heterogeneous anti-tumor effects across tumor cohorts, inactive cohorts are pruned, and the potentially active cohorts are pooled together to gain study power. For a prospective design with a target power, the total sample size across all cohorts is minimized; or for an ad hoc analysis with pre-specified sample size for each cohort, the pruning criteria are optimized to achieve maximum power. The global type I error is controlled after proper multiplicity adjustment, and a penalty adjusted significance level is used for the pooled test. RESULTS: Simulation studies show that the proposed optimal design has desirable operating characteristics in increasing the overall power and detecting more true positive tumor cohorts. CONCLUSION: The proposed optimal design and analysis strategy provides a practical approach to design and analyze heterogeneous efficacy expansion cohorts in a basket setting with global type I and type II error being controlled.

Clinical trials in a COVID-19 pandemic: Shared infrastructure for continuous learning in a rapidly changing landscape.

BACKGROUND: Clinical trials, conducted efficiently and with the utmost integrity, are a key component in identifying effective vaccines, therapies, and other interventions urgently needed to solve the COVID-19 crisis. Yet launching and implementing trials with the rigor necessary to produce convincing results is a complicated and time-consuming process. Balancing rigor and efficiency involves relying on designs that employ flexible features to respond to a fast-changing landscape, measuring valid endpoints that result in translational actions and disseminating findings in a timely manner. We describe the challenges involved in creating infrastructure with potential utility for shared learning. METHODS: We have established a shared infrastructure that borrows strength across multiple trials. The infrastructure includes an endpoint registry to aid in selecting appropriate endpoints, a registry to facilitate establishing a Data & Safety Monitoring Board, common data collection instruments, a COVID-19 dedicated design and analysis team, and a pragmatic platform protocol, among other elements. RESULTS: The authors have relied on the shared infrastructure for six clinical trials for which they serve as the Data Coordinating Center and have a design and analysis team comprising 15 members who are dedicated to COVID-19. The authors established a pragmatic platform to simultaneously investigate multiple treatments for the outpatient with adaptive features to add or drop treatment arms. CONCLUSION: The shared infrastructure provides appealing opportunities to evaluate disease in a more robust manner with fewer resources and is especially valued during a pandemic where efficiency in time and resources is crucial. The most important element of the shared infrastructure is the pragmatic platform. While it may be the most challenging of the elements to establish, it may provide the greatest benefit to both patients and researchers.

[Application of umbrella design and basket design in evidence-based research for traditional Chinese medicine].

This paper introduced the basic definition, application scope, advantages and challenges of the master protocol, basket design, umbrella design and platform trial, and put forward the idea of using master protocol, basket design and umbrella design in Chinese medicine(CM) by considering the characteristics of CM and research experiences. The author pointed out that master protocol, basket design and umbrella design, as a high-efficiency research and design strategy, can be used in the clinical research on the treatment of the same disease with different therapies, the treatment of different diseases with the same therapy and the combination of diseases and CM syndromes. In particular, the exploration from the classification of CM syndromes can supplement the gaps in the cli-nical research on CM syndromes. In the application of such designs, it is also necessary to pay attention to their potential challenges and develop reasonable and feasible plans on research implementation, management and statistical analysis in advance to meet these challenges.

The precision interventions for severe and/or exacerbation-prone asthma (PrecISE) adaptive platform trial: statistical considerations.

The Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) study is an adaptive platform trial designed to investigate novel interventions to severe asthma. The study is conducted under a master protocol and utilizes a crossover design with each participant receiving up to five interventions and at least one placebo. Treatment assignments are based on the patients' biomarker profiles and precision health methods are incorporated into the interim and final analyses. We describe key elements of the PrecISE study including the multistage adaptive enrichment strategy, early stopping of an intervention for futility, power calculations, and the primary analysis strategy.

Umbrella and basket trials in oncology: ethical challenges.

BACKGROUND: Novel precision oncology trial designs, such as basket and umbrella trials, are designed to test new anticancer agents in more effective and affordable ways. However, they present some ethical concerns referred to scientific validity, risk-benefit balance and informed consent. Our aim is to discuss these issues in basket and umbrella trials, giving examples of two ongoing cancer trials: NCI-MATCH (National Cancer Institute - Molecular Analysis for Therapy Choice) and Lung-MAP (Lung Cancer Master Protocol) study. MAIN BODY: We discuss three ethical requirements for clinical trials which may be challenged in basket and umbrella trial designs. Firstly, we consider scientific validity. Thanks to the new trial designs, patients with rare malignancies have the opportunity to be enrolled and benefit from the trial, but due to insufficient accrual, the trial may generate clinically insignificant findings. Inadequate sample size in study arms and the use of surrogate endpoints may result in a drug approval without confirmed efficacy. Moreover, complexity, limited quality and availability of tumor samples may not only introduce bias and result in unreliable and unrepresentative findings, but also can potentially harm patients and assign them to an inappropriate therapy arm. Secondly, we refer to benefits and risks. Novel clinical trials can gain important knowledge on the variety of tumors, which can be used in future trials to develop effective therapies. However, they offer limited direct benefits to patients. All potential participants must wait about 2 weeks for the results of the genetic screening, which may be stressful and produce anxiety. The enrollment of patients whose tumors harbor multiple mutations in treatments matching a single mutation may be controversial. As to informed consent - the third requirement we discuss, the excessive use of phrases like "personalized medicine", "tailored therapy" or "precision oncology" might be misleading and cause personal convictions that the study protocol is designed to fulfill the individual health-related needs of participants. CONCLUSIONS: We suggest that further approaches should be implemented to enhance scientific validity, reduce misunderstandings and risks, thus maximizing the benefits to society and to trial participants.

Accelerating Therapeutic Development through Innovative Trial Design in Colorectal Cancer.

OPINION STATEMENT: Current trial design is challenged by the advancement of technologies that have enabled deeper understanding of the molecular drivers of colorectal cancer (CRC). The speed of trial testing and the ability to test larger volumes of promising novel agents in the face of smaller populations identified by molecular profiling are challenges posed to clinical studies. Master protocols that utilize umbrella designs are equipped to deal with potential biomarker and matched treatments simultaneously. Although complex in nature, they increase trial efficiency by utilizing shared screening platforms, test multiple treatments together, and simplify regulatory submission and reporting under a common protocol. Emerging technologies such as circulating tumor DNA (ctDNA) may help speed up adjuvant trials. These studies have been traditionally slow to complete due to low event rates and the high numbers needed to recruit. ctDNA used as a surrogate for minimal residual disease (MRD) and as an early marker of relapse may help counter some of these factors that deter innovation in this setting. Finally, in the era of precision medicine, surgery should not be forgotten as the only potentially curative option to date in metastatic disease. Five-year overall survival following resection of liver metastasis exceeds what can be achieved with chemotherapy alone in selected cases. Surgical advances have lowered morbidity and allow for greater resection volumes and repeated interventions. Although historically challenging, a well-designed randomized surgical intervention trial would greatly facilitate moving single-institution guidelines reported by case series into wider clinical practice.

Enhancing Insights into Pulmonary Vascular Disease through a Precision Medicine Approach. A Joint NHLBI-Cardiovascular Medical Research and Education Fund Workshop Report.

The Division of Lung Diseases of the NHLBI and the Cardiovascular Medical Education and Research Fund held a workshop to discuss how to leverage the anticipated scientific output from the recently launched "Redefining Pulmonary Hypertension through Pulmonary Vascular Disease Phenomics" (PVDOMICS) program to develop newer approaches to pulmonary vascular disease. PVDOMICS is a collaborative, protocol-driven network to analyze all patient populations with pulmonary hypertension to define novel pulmonary vascular disease (PVD) phenotypes. Stakeholders, including basic, translational, and clinical investigators; clinicians; patient advocacy organizations; regulatory agencies; and pharmaceutical industry experts, joined to discuss the application of precision medicine to PVD clinical trials. Recommendations were generated for discussion of research priorities in line with NHLBI Strategic Vision Goals that include: (1) A national effort, involving all the stakeholders, should seek to coordinate biosamples and biodata from all funded programs to a web-based repository so that information can be shared and correlated with other research projects. Example programs sponsored by NHLBI include PVDOMICS, Pulmonary Hypertension Breakthrough Initiative, the National Biological Sample and Data Repository for PAH, and the National Precision Medicine Initiative. (2) A task force to develop a master clinical trials protocol for PVD to apply precision medicine principles to future clinical trials. Specific features include: (a) adoption of smaller clinical trials that incorporate biomarker-guided enrichment strategies, using adaptive and innovative statistical designs; and (b) development of newer endpoints that reflect well-defined and clinically meaningful changes. (3) Development of updated and systematic variables in imaging, hemodynamic, cellular, genomic, and metabolic tests that will help precisely identify individual and shared features of PVD and serve as the basis of novel phenotypes for therapeutic interventions.

Efficiencies of platform clinical trials: A vision of the future.

BACKGROUND: A "platform trial" is a clinical trial with a single master protocol in which multiple treatments are evaluated simultaneously. Adaptive platform designs offer flexible features such as dropping treatments for futility, declaring one or more treatments superior, or adding new treatments to be tested during the course of a trial. METHODS: A simulation study explores the efficiencies of various platform trial designs relative to a traditional two-arm strategy. RESULTS: Platform trials can find beneficial treatments with fewer patients, fewer patient failures, less time, and with greater probability of success than a traditional two-arm strategy. CONCLUSION: In an era of personalized medicine, platform trials provide the innovation needed to efficiently evaluate modern treatments.

rBMA: A robust Bayesian Model Averaging Method for phase II basket trials based on informative mixture priors.

Oncology drug research in the last few decades has been driven by the development of targeted agents. In the era of targeted therapies, basket trials are often used to test the antitumor activity of a novel treatment in multiple indications sharing the same genomic alteration. As patient population are further fragmented into biomarker-defined subgroups in basket trials, novel statistical methods are needed to facilitate cross-indication learning to improve the statistical power in basket trial design. Here we propose a robust Bayesian model averaging (rBMA) technique for the design and analysis of phase II basket trials. We consider the posterior distribution of each indication (basket) as the weighted average of three different models which only differ in their priors (enthusiastic, pessimistic and non-informative). The posterior weights of these models are determined based on the effect of the experimental treatment in all the indications tested. In early phase oncology trials, different binary endpoints might be chosen for different indications (objective response, disease control or PFS at landmark times), which makes it even more challenging to borrow information across indications. Compared to previous approaches, the proposed method has the flexibility to support cross-indication learning in the presence of mixed endpoints. We evaluate and compare the performance of the proposed rBMA approach to competing approaches in simulation studies. R scripts to implement the proposed method are available at https://github.com/xwang317/rBMA.

Unlocking the Potential: A Systematic Review of Master Protocol in Pediatrics.

The use of master protocols allows for innovative approaches to clinical trial designs, potentially enabling new approaches to operations and analytics and creating value for patients and drug developers. Pediatric research has been conducted for many decades, but the use of novel designs such as master protocols in pediatric research is not well understood. This study aims to provide a systematic review on the utilization of master protocols in pediatric drug development. A search was performed in September 2022 using two data sources (PubMed and ClinicalTrials.gov) and included studies conducted in the past10 years. General study information was extracted such as study type, study status, therapeutic area, and clinical trial phase. Study characteristics that are specific to pediatric studies (such as age of the participants and pediatric drug dosing) and important study design elements (such as number of test drug arms and whether randomization and/or concurrent control was used) were also collected. Our results suggest that master protocol studies are being used in pediatrics, with platform and basket trials more common than umbrella trials. Most of this experience is in oncology and early phase studies. There is a rise in the use starting in 2020, largely in oncology and COVID-19 trials. However, adoption of master protocols in pediatric clinical research is still on a small scale and could be substantially expanded. Work is required to further understand the barriers in implementing pediatric master protocols, from setting up infrastructure to interpreting study findings.

A collaborative network trial to evaluate the effectiveness of implementation strategies to maximize adoption of a school-based healthy lunchbox program: a study protocol.

Introduction: An important impediment to the large-scale adoption of evidence-based school nutrition interventions is the lack of evidence on effective strategies to implement them. This paper describes the protocol for a "Collaborative Network Trial" to support the simultaneous testing of different strategies undertaken by New South Wales Local Health Districts to facilitate the adoption of an effective school-based healthy lunchbox program ('SWAP IT'). The primary objective of this study is to assess the effectiveness of different implementation strategies to increase school adoption of the SWAP across New South Wales Local Health Districts. Methods: Within a Master Protocol framework, a collaborative network trial will be undertaken. Independent randomized controlled trials to test implementation strategies to increase school adoption of SWAP IT within primary schools in 10 different New South Wales Local Health Districts will occur. Schools will be randomly allocated to either the intervention or control condition. Schools allocated to the intervention group will receive a combination of implementation strategies. Across the 10 participating Local Health Districts, six broad strategies were developed and combinations of these strategies will be executed over a 6 month period. In six districts an active comparison group (containing one or more implementation strategies) was selected. The primary outcome of the trial will be adoption of SWAP IT, assessed via electronic registration records captured automatically following online school registration to the program. The primary outcome will be assessed using logistic regression analyses for each trial. Individual participant data component network meta-analysis, under a Bayesian framework, will be used to explore strategy-covariate interactions; to model additive main effects (separate effects for each component of an implementation strategy); two way interactions (synergistic/antagonistic effects of components), and full interactions. Discussion: The study will provide rigorous evidence of the effects of a variety of implementation strategies, employed in different contexts, on the adoption of a school-based healthy lunchbox program at scale. Importantly, it will also provide evidence as to whether health service-centered, collaborative research models can rapidly generate new knowledge and yield health service improvements. Clinical trial registration: This trial is registered prospectively with the Australian New Zealand Clinical Trials Registry (ACTRN12623000558628).

Master protocols and other innovative trial designs in inflammation and immunology to expedite clinical drug development.

Master protocol designs, such as umbrella and basket studies, allow multiple compounds or multiple target populations to be evaluated simultaneously within a single protocol, and have been widely adopted in oncology clinical trials. These novel designs can also be applied in other therapeutic areas, where they could have several benefits over conducting traditional randomized controlled trials. Here, we detail Pfizer's recent implementations of master protocol designs in inflammation and immunology clinical studies, focusing on the opportunities for cost and resource savings and how these designs can expedite the time required to bring new treatments to patients in need.