1
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Shergina E, Richter KP, Daley CM, Faseru B, Choi WS, Gajewski BJ. Using Bayesian hierarchical models for controlled post hoc subgroup analysis of clinical trials: application to smoking cessation treatment in American Indians and Alaska Natives. J Biopharm Stat 2024; 34:513-525. [PMID: 37417836 PMCID: PMC10771533 DOI: 10.1080/10543406.2023.2233598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/01/2023] [Indexed: 07/08/2023]
Abstract
Clinical trials powered to detect subgroup effects provide the most reliable data on heterogeneity of treatment effect among different subpopulations. However, pre-specified subgroup analysis is not always practical and post hoc analysis results should be examined cautiously. Bayesian hierarchical modelling provides grounds for defining a controlled post hoc analysis plan that is developed after seeing outcome data for the population but before unblinding the outcome by subgroup. Using simulation based on the results from a tobacco cessation clinical trial conducted among the general population, we defined an analysis plan to assess treatment effect among American Indians and Alaska Natives (AI/AN) enrolled in the study. Patients were randomized into two arms using Bayesian adaptive design. For the opt-in arm, clinicians offered a cessation treatment plan after verifying that a patient was ready to quit. For the opt-out arm, clinicians provided all participants with free cessation medications and referred them to a Quitline. The study was powered to test a hypothesis of significantly higher quit rates for the opt-out arm at one-month post randomization. Overall, one-month abstinence rates were 15.9% and 21.5% (opt-in and opt-out arm, respectively). For AI/AN, one-month abstinence rates were 10.2% and 22.0% (opt-in and opt-out arm, respectively). The posterior probability that the abstinence rate in the treatment arm is higher is 0.96, indicating that AI/AN demonstrate response to treatment at almost the same probability as the whole population.
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Affiliation(s)
- Elena Shergina
- Department of Biostatistics & Data Science, University of Kansas Cancer Center, 3901 Rainbow Boulevard, Kansas City, KS, USA
| | - Kimber P. Richter
- Department of Population Health, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS, USA
| | - Christine Makosky Daley
- Department of Community and Health Population, Lehigh University, 27 Memorial Dr W, Bethlehem, PA, USA
| | - Babalola Faseru
- Department of Population Health, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS, USA
| | - Won S. Choi
- Department of Community and Health Population, Lehigh University, 27 Memorial Dr W, Bethlehem, PA, USA
| | - Byron J. Gajewski
- Department of Biostatistics & Data Science, University of Kansas Cancer Center, 3901 Rainbow Boulevard, Kansas City, KS, USA
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2
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Tu Y, Renfro LA. Biomarker-driven basket trial designs: origins and new methodological developments. J Biopharm Stat 2024:1-13. [PMID: 38832723 DOI: 10.1080/10543406.2024.2358806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 05/12/2024] [Indexed: 06/05/2024]
Abstract
Due to increased use of gene sequencing techniques, understanding of cancer on a molecular level has evolved, in terms of both diagnosis and evaluation in response to initial therapies. In parallel, clinical trials meant to evaluate molecularly-driven interventions through assessment of both treatment effects and putative predictive biomarker effects are being employed to advance the goals of precision medicine. Basket trials investigate one or more biomarker-targeted therapies across multiple cancer types in a tumor location agnostic fashion. The review article offers an overview of the traditional forms of such designs, the practical challenges facing each type of design, and then review novel adaptations proposed in the last few years, categorized into Bayesian and Classical Frequentist perspectives. The review article concludes by summarizing potential advantages and limitations of the new trial design solutions.
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Affiliation(s)
- Yue Tu
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
| | - Lindsay A Renfro
- Department of Population and Public Health Sciences, University of Southern California and Children's Oncology Group, Los Angeles, California, USA
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3
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Li L, Ivanova A. Isotonic design for single-arm biomarker stratified trials. Stat Methods Med Res 2024; 33:945-952. [PMID: 38573793 PMCID: PMC11162092 DOI: 10.1177/09622802241238978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
In single-arm trials with a predefined subgroup based on baseline biomarkers, it is often assumed that a biomarker defined subgroup, the biomarker positive subgroup, has the same or higher response to treatment compared to its complement, the biomarker negative subgroup. The goal is to determine if the treatment is effective in each of the subgroups or in the biomarker positive subgroup only or not effective at all. We propose the isotonic stratified design for this problem. The design has a joint set of decision rules for biomarker positive and negative subjects and utilizes joint estimation of response probabilities using assumed monotonicity of response between the biomarker negative and positive subgroups. The new design reduces the sample size requirement when compared to running two Simon's designs in each biomarker positive and negative. For example, the new design requires 23%-35% fewer patients than running two Simon's designs for scenarios we considered. Alternatively, the new design allows evaluating the response probability in both biomarker negative and biomarker positive subgroups using only 40% more patients needed for running Simon's design in the biomarker positive subgroup only.
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Affiliation(s)
- Lang Li
- Department of Biostatistics, CB #7420, The University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - Anastasia Ivanova
- Department of Biostatistics, CB #7420, The University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, NC, USA
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4
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Zhang J, Lin R, Chen X, Yan F. Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses. Clin Trials 2024; 21:308-321. [PMID: 38243401 PMCID: PMC11132956 DOI: 10.1177/17407745231212193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
Abstract
In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.
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Affiliation(s)
- Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xin Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
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5
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Lee SY. Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review. BMC Med Res Methodol 2024; 24:110. [PMID: 38714936 PMCID: PMC11077897 DOI: 10.1186/s12874-024-02235-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/24/2024] [Indexed: 05/12/2024] Open
Abstract
Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.
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Affiliation(s)
- Se Yoon Lee
- Department of Statistics, Texas A &M University, 3143 TAMU, College Station, TX, 77843, USA.
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6
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Cui C, Tang Y, Zhang W. Deep Survival Analysis With Latent Clustering and Contrastive Learning. IEEE J Biomed Health Inform 2024; 28:3090-3101. [PMID: 38319782 DOI: 10.1109/jbhi.2024.3362850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Survival analysis is employed to analyze the time before the event of interest occurs, which is broadly applied in many fields. The existence of censored data with incomplete supervision information about survival outcomes is one key challenge in survival analysis tasks. Although some progress has been made on this issue recently, the present methods generally treat the instances as separate ones while ignoring their potential correlations, thus rendering unsatisfactory performance. In this study, we propose a novel Deep Survival Analysis model with latent Clustering and Contrastive learning (DSACC). Specifically, we jointly optimize representation learning, latent clustering and survival prediction in a unified framework. In this way, the clusters distribution structure in latent representation space is revealed, and meanwhile the structure of the clusters is well incorporated to improve the ability of survival prediction. Besides, by virtue of the learned clusters, we further propose a contrastive loss function, where the uncensored data in each cluster are set as anchors, and the censored data are treated as positive/negative sample pairs according to whether they belong to the same cluster or not. This design enables the censored data to make full use of the supervision information of the uncensored samples. Through extensive experiments on four popular clinical datasets, we demonstrate that our proposed DSACC achieves advanced performance in terms of both C-index (0.6722, 0.6793, 0.6350, and 0.7943) and Integrated Brier Score (IBS) (0.1616, 0.1826, 0.2028, and 0.1120).
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7
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Zhang P, Li XN. A win ratio-based framework to combine multiple clinical endpoints in exploratory basket trials. J Biopharm Stat 2024; 34:251-259. [PMID: 38252040 DOI: 10.1080/10543406.2023.2187819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 03/01/2023] [Indexed: 03/11/2023]
Abstract
In contemporary exploratory phase of oncology drug development, there has been an increasing interest in evaluating investigational drug or drug combination in multiple tumor indications in a single basket trial to expedite drug development. There has been extensive research on more efficiently borrowing information across tumor indications in early phase drug development including Bayesian hierarchical modeling and the pruning-and-pooling methods. Despite the fact that the Go/No-Go decision for subsequent Phase 2 or Phase 3 trial initiation is almost always a multi-facet consideration, the statistical literature of basket trial design and analysis has largely been limited to a single binary endpoint. In this paper we explore the application of considering clinical priorities of multiple endpoints based on matched win ratio to the basket trial design and analysis. The control arm data will be simulated for each tumor indication based on the corresponding null assumptions that could be heterogeneous across tumor indications. The matched win ratio matching on the tumor indication can be performed for individual tumor indication, pooled data, or the pooled data after pruning depending on whether an individual evaluation or a simple pooling or a pruning-and-pooling method is used. We conduct the simulation studies to evaluate the performance of proposed win ratio-based framework and the results suggest the proposed framework could provide desirable operating characteristics.
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Affiliation(s)
- Pingye Zhang
- Global Statistics and Data Science, BeiGene, Ltd, Ridgefield Park, New Jersey, USA
| | - Xiaoyun Nicole Li
- Global Statistics and Data Science, BeiGene, Ltd, Ridgefield Park, New Jersey, USA
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8
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Zhang Y, Chu C, Beckman RA, Gao L, Laird G, Yi B. A confirmatory basket design considering non-inferiority and superiority testing. J Biopharm Stat 2024; 34:205-221. [PMID: 36988397 DOI: 10.1080/10543406.2023.2192781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 03/14/2023] [Indexed: 03/30/2023]
Abstract
For multiple rare diseases as defined by a common biomarker signature, or a disease with multiple disease subtypes of low frequency, it is often possible to provide confirmatory evidence for these disease or subtypes (baskets) as a combined group. A novel drug, as a second generation, may have marginal improvement in efficacy overall but superior efficacy in some baskets. In this situation, it is appealing to test hypotheses of both non-inferiority overall and superiority on certain baskets. The challenge is designing a confirmatory study efficient to address multiple questions in one trial. A two-stage adaptive design is proposed to test the non-inferiority hypothesis at the interim stage, followed by pruning and pooling before testing a superiority hypothesis at the final stage. Such a design enables an efficient and novel registration pathway, including an early claim of non-inferiority followed by a potential label extension with superiority on certain baskets and an improved benefit-risk profile demonstrated by longer term efficacy and safety data. Operating characteristics of this design are examined by simulation studies, and its appealing features make it ready for use in a confirmatory setting, especially in emerging markets, where both the need and the possibility for efficient use of resources may be the greatest.
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Affiliation(s)
- Yaohua Zhang
- Department of Biometrics, Vertex Pharmaceuticals Inc, Boston, Massachusetts, USA
| | - Chenghao Chu
- Department of Biometrics, Vertex Pharmaceuticals Inc, Boston, Massachusetts, USA
| | - Robert A Beckman
- Departments of Oncology and of Biostatistics Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, USA
| | - Lei Gao
- Department of Biostatisticis and Programming, Moderna, Cambridge, Massachusetts, USA
| | - Glen Laird
- Department of Biometrics, Vertex Pharmaceuticals Inc, Boston, Massachusetts, USA
| | - Bingming Yi
- Department of Biometrics, Vertex Pharmaceuticals Inc, Boston, Massachusetts, USA
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9
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Chi X, Yuan Y, Yu Z, Lin R. A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints. Biom J 2024; 66:e2300122. [PMID: 38368277 DOI: 10.1002/bimj.202300122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 11/05/2023] [Accepted: 12/29/2023] [Indexed: 02/19/2024]
Abstract
A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk-benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial-normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operating characteristics.
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Affiliation(s)
- Xiaohan Chi
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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10
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Takeda K, Hashimoto A, Liu S, Rong A. A basket trial design based on constrained hierarchical Bayesian model for latent subgroups. J Biopharm Stat 2024:1-12. [PMID: 38369872 DOI: 10.1080/10543406.2024.2311851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/24/2024] [Indexed: 02/20/2024]
Abstract
It is well known a basket trial consisting of multiple cancer types has the potential of borrowing strength across the baskets defined by the cancer types, leading to an efficient design in terms of sample size and trial duration. The treatment effects in those baskets are often heterogeneous and categorized by the cancer types being sensitive or insensitive to the treatment. Hence, the assumption of exchangeability in many existing basket trials may be violated, and there is a need to design trials without this assumption. In this paper, we simplify the constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) for two classifiers to deal with the potential heterogeneity of treatment effects due to the single classifier of the cancer type. Different baskets are aggregated into subgroups using a latent subgroup modeling approach. The treatment effects are similar and exchangeable to facilitate information borrowing within each latent subgroup. Applying the simplified CHBM-LS approach to the real basket trials where baskets defined by only cancer types shows better performance than other available approaches. Further simulation study also demonstrates this CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios.
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Affiliation(s)
- Kentaro Takeda
- Data Science, Astellas Pharma Global Development Inc, Northbrook, Illinois, USA
| | | | - Shufang Liu
- Oncology Biostatistics, Gilead Sciences Inc, Foster City, California, USA
| | - Alan Rong
- Oncology Biostatistics, Gilead Sciences Inc, Foster City, California, USA
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11
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Lu CC, Beckman RA, Li XN, Zhang W, Jiang Q, Marchenko O, Sun Z, Tian H, Ye J, Yuan SS, Yung G. Tumor-Agnostic Approvals: Insights and Practical Considerations. Clin Cancer Res 2024; 30:480-488. [PMID: 37792436 DOI: 10.1158/1078-0432.ccr-23-1340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/05/2023] [Accepted: 10/03/2023] [Indexed: 10/05/2023]
Abstract
Since the first approval of a tumor-agnostic indication in 2017, a total of seven tumor-agnostic indications involving six drugs have received approval from the FDA. In this paper, the master protocol subteam of the Statistical Methods in Oncology Scientific Working Group, Biopharmaceutical Session, American Statistical Association, provides a comprehensive summary of these seven tumor-agnostic approvals, describing their mechanisms of action; biomarker prevalence; study design; companion diagnostics; regulatory aspects, including comparisons of global regulatory requirements; and health technology assessment approval. Also discussed are practical considerations relating to the regulatory approval of tumor-agnostic indications, specifically (i) recommendations for the design stage to mitigate the risk that exceptions may occur if a treatment is initially hypothesized to be effective for all tumor types and (ii) because drug development continues after approval of a tumor-agnostic indication, recommendations for further development of tumor-specific indications in first-line patients in the setting of a randomized confirmatory basket trial, acknowledging the challenges in this area. These recommendations and practical considerations may provide insights for the future development of drugs for tumor-agnostic indications.
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Affiliation(s)
| | - Robert A Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC
| | | | | | - Qi Jiang
- Biometrics, Seagen, Bothell, Washington
| | - Olga Marchenko
- Statistics and Data Insights, Bayer, Whippany, New Jersey
| | - Zhiping Sun
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey
| | - Hong Tian
- Global Statistics and Data Sciences, BeiGene, Fulton, Maryland
| | - Jingjing Ye
- Global Statistics and Data Sciences, BeiGene, Fulton, Maryland
| | - Shuai Sammy Yuan
- Oncology Statistics, GlaxoSmithKline, Collegeville, Pennsylvania
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12
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Kanapka L, Ivanova A. A frequentist design for basket trials using adaptive lasso. Stat Med 2024; 43:156-172. [PMID: 37919834 DOI: 10.1002/sim.9947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/26/2023] [Accepted: 09/29/2023] [Indexed: 11/04/2023]
Abstract
A basket trial aims to expedite the drug development process by evaluating a new therapy in multiple populations within the same clinical trial. Each population, referred to as a "basket", can be defined by disease type, biomarkers, or other patient characteristics. The objective of a basket trial is to identify the subset of baskets for which the new therapy shows promise. The conventional approach would be to analyze each of the baskets independently. Alternatively, several Bayesian dynamic borrowing methods have been proposed that share data across baskets when responses appear similar. These methods can achieve higher power than independent testing in exchange for a risk of some inflation in the type 1 error rate. In this paper we propose a frequentist approach to dynamic borrowing for basket trials using adaptive lasso. Through simulation studies we demonstrate adaptive lasso can achieve similar power and type 1 error to the existing Bayesian methods. The proposed approach has the benefit of being easier to implement and faster than existing methods. In addition, the adaptive lasso approach is very flexible: it can be extended to basket trials with any number of treatment arms and any type of endpoint.
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Affiliation(s)
- Lauren Kanapka
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anastasia Ivanova
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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13
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Zhou T, Ji Y. Bayesian Methods for Information Borrowing in Basket Trials: An Overview. Cancers (Basel) 2024; 16:251. [PMID: 38254740 PMCID: PMC10813856 DOI: 10.3390/cancers16020251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024] Open
Abstract
Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.
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Affiliation(s)
- Tianjian Zhou
- Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA
| | - Yuan Ji
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637, USA
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14
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Zang M, Liu R. Generalized triple outcome decision-making in basket trials. J Biopharm Stat 2024:1-17. [PMID: 38166528 DOI: 10.1080/10543406.2023.2296054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/04/2023] [Indexed: 01/04/2024]
Abstract
Making the go/no-go decision is critical in Phase II (or Ib) clinical trials. The conventional decision-making framework based on a binary hypothesis testing has been gradually replaced by the TODeM (Triple Outcome Decision-Making) which has three zones of outcomes: go, no-go, and consider. The TODeM provides more flexibility in decision-making with considering both of statistical significance and clinical relevance. However, Bayesian methods (e.g. EXNEX, MUCE, etc.) for the information borrowing are still based on the binary decision-making framework. We propose a new decision-making process G-TODeM (Generalized Triple Outcome Decision-Making) to apply those Bayesian methods with information borrowing across different cohorts to the TODeM framework. Essentially, the information borrowed from other cohorts can shrink the consider zone of the inference cohort.
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Affiliation(s)
- Miao Zang
- Global Statistics & Data Science (GSDS), BeiGene, Beijing, China
| | - Rui Liu
- Global Statistics & Data Science (GSDS), BeiGene, Beijing, China
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15
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Mahar RK, McGlothlin A, Dymock M, Lee TC, Lewis RJ, Lumley T, Mora J, Price DJ, Saville BR, Snelling T, Turner R, Webb SA, Davis JS, Tong SYC, Marsh JA. A blueprint for a multi-disease, multi-domain Bayesian adaptive platform trial incorporating adult and paediatric subgroups: the Staphylococcus aureus Network Adaptive Platform trial. Trials 2023; 24:795. [PMID: 38057927 PMCID: PMC10699085 DOI: 10.1186/s13063-023-07718-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 10/05/2023] [Indexed: 12/08/2023] Open
Abstract
The Staphylococcus aureus Network Adaptive Platform (SNAP) trial is a multifactorial Bayesian adaptive platform trial that aims to improve the way that S. aureus bloodstream infection, a globally common and severe infectious disease, is treated. In a world first, the SNAP trial will simultaneously investigate the effects of multiple intervention modalities within multiple groups of participants with different forms of S. aureus bloodstream infection. Here, we formalise the trial structure, modelling approach, and decision rules that will be used for the SNAP trial. By summarising the statistical principles governing the design, our hope is that the SNAP trial will serve as an adaptable template that can be used to improve comparative effectiveness research efficiency in other disease areas.Trial registration NCT05137119 . Registered on 30 November 2021.
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Affiliation(s)
- Robert K Mahar
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia.
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia.
| | | | - Michael Dymock
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Todd C Lee
- Division of Infectious Diseases, Department of Medicine, McGill University, Montreal, Canada
| | - Roger J Lewis
- Berry Consultants LLC, Austin, Texas, USA
- Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, California, USA
| | - Thomas Lumley
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Jocelyn Mora
- Department of Infectious Diseases, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - David J Price
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
- Department of Infectious Diseases, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
| | - Benjamin R Saville
- Berry Consultants LLC, Austin, Texas, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Tom Snelling
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, Western Australia, Australia
- Department of Infectious Diseases, Perth Children's Hospital, Perth, Western Australia, Australia
- Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Rebecca Turner
- Medical Research Council Clinical Trials Unit at University College London, London, United Kingdom
| | - Steven A Webb
- St John of God Healthcare, Perth, Western Australia, Australia
- Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Joshua S Davis
- Department of Infectious Diseases, John Hunter Hospital, Newcastle, New South Wales, Australia
- Menzies School of Health Research, Royal Darwin Hospital, Darwin, Northern Territory, Australia
| | - Steven Y C Tong
- Department of Infectious Diseases, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia
- Victorian Infectious Diseases Service, Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Julie A Marsh
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, Western Australia, Australia
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16
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Yang P, Zhao Y, Nie L, Vallejo J, Yuan Y. SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials. Biometrics 2023; 79:2857-2868. [PMID: 37721513 PMCID: PMC10842647 DOI: 10.1111/biom.13927] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 07/27/2023] [Indexed: 09/19/2023]
Abstract
Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a noninformative prior. However, prespecifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which is often unknown beforehand, posing a significant obstacle to the application and acceptance of mixture priors. To address this challenge, we introduce self-adapting mixture (SAM) priors that determine the mixing weight using likelihood ratio test statistics or Bayes factors. SAM priors are data-driven and self-adapting, favoring the informative (noninformative) prior component when there is little (substantial) evidence of prior-data conflict. Consequently, SAM priors achieve dynamic information borrowing. We demonstrate that SAM priors exhibit desirable properties in both finite and large samples and achieve information-borrowing consistency. Moreover, SAM priors are easy to compute, data-driven, and calibration-free, mitigating the risk of data dredging. Numerical studies show that SAM priors outperform existing methods in adopting prior-data conflicts effectively. We developed R package "SAMprior" and web application that are freely available at CRAN and www.trialdesign.org to facilitate the use of SAM priors.
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Affiliation(s)
- Peng Yang
- Department of Statistics, Rice University, Houston, Texas, USA
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Yuansong Zhao
- Department of Biostatistics, The University of Texas Health Science Center, Houston, Texas, USA
| | - Lei Nie
- Center for Drug Evaluation and Research, Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Jonathon Vallejo
- Center for Drug Evaluation and Research, Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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17
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Hattori S, Morita S. Frequentist analysis of basket trials with one-sample Mantel-Haenszel procedures. Stat Med 2023; 42:4824-4849. [PMID: 37670577 DOI: 10.1002/sim.9890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 06/09/2023] [Accepted: 08/16/2023] [Indexed: 09/07/2023]
Abstract
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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Affiliation(s)
- Satoshi Hattori
- Department of Biomedical Statistics, Graduate School of Medicine, Osaka University, Osaka, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan
| | - Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
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18
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Kasim A, Bean N, Hendriksen SJ, Chen TT, Zhou H, Psioda MA. Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity. Front Oncol 2023; 13:1266286. [PMID: 38033501 PMCID: PMC10684308 DOI: 10.3389/fonc.2023.1266286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/13/2023] [Indexed: 12/02/2023] Open
Abstract
Background Basket trials are increasingly used in oncology drug development for early signal detection, accelerated tumor-agnostic approvals, and prioritization of promising tumor types in selected patients with the same mutation or biomarker. Participants are grouped into so-called baskets according to tumor type, allowing investigators to identify tumors with promising responses to treatment for further study. However, it remains a question as to whether and how much the adoption of basket trial designs in oncology have translated into patient benefits, increased pace and scale of clinical development, and de-risking of downstream confirmatory trials. Methods Innovation in basket trial design and analysis includes methods that borrow information across tumor types to increase the quality of statistical inference within each tumor type. We build on the existing systematic reviews of basket trials in oncology to discuss the current practices and landscape. We conceptually illustrate recent innovative methods for basket trials, with application to actual data from recently completed basket trials. We explore and discuss the extent to which innovative basket trials can be used to de-risk future trials through their ability to aid prioritization of promising tumor types for subsequent clinical development. Results We found increasing adoption of basket trial design in oncology, but largely in the design of single-arm phase II trials with a very low adoption of innovative statistical methods. Furthermore, the current practice of basket trial design, which does not consider its impact on the clinical development plan, may lead to a missed opportunity in improving the probability of success of a future trial. Gating phase II with a phase Ib basket trial reduced the size of phase II trials, and losses in the probability of success as a result of not using innovative methods may not be recoverable by running a larger phase II trial. Conclusion Innovative basket trial methods can reduce the size of early phase clinical trials, with sustained improvement in the probability of success of the clinical development plan. We need to do more as a community to improve the adoption of these methods.
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Affiliation(s)
- Adetayo Kasim
- Disease Area Strategy, Oncology Biostatistics, GlaxoSmithKline, Brentford, United Kingdom
| | - Nathan Bean
- Statistics and Data Science – Innovation Hub, GlaxoSmithKline, Philadelphia, PA, United States
| | - Sarah Jo Hendriksen
- Medical and Market Access, Oncology Biostatistics, GlaxoSmithKline, Stevenage, United Kingdom
| | - Tai-Tsang Chen
- Disease Area Strategy, Oncology Biostatistics, GlaxoSmithKline, Philadelphia, PA, United States
| | - Helen Zhou
- Disease Area Strategy, Oncology Biostatistics, GlaxoSmithKline, Philadelphia, PA, United States
| | - Matthew A. Psioda
- Statistics and Data Science – Innovation Hub, GlaxoSmithKline, Philadelphia, PA, United States
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19
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Daniells L, Mozgunov P, Bedding A, Jaki T. A comparison of Bayesian information borrowing methods in basket trials and a novel proposal of modified exchangeability-nonexchangeability method. Stat Med 2023; 42:4392-4417. [PMID: 37614070 PMCID: PMC10962580 DOI: 10.1002/sim.9867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/12/2023] [Accepted: 07/24/2023] [Indexed: 08/25/2023]
Abstract
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.
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Affiliation(s)
- Libby Daniells
- STOR‐i Centre for Doctoral Training, Department of Mathematics and StatisticsLancaster UniversityLancasterUK
| | - Pavel Mozgunov
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
| | | | - Thomas Jaki
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Faculty of Informatics and Data ScienceUniversity of RegensburgRegensburgGermany
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20
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Vinnat V, Annane D, Chevret S. Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros. J Pers Med 2023; 13:1560. [PMID: 38003875 PMCID: PMC10672716 DOI: 10.3390/jpm13111560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 11/26/2023] Open
Abstract
Precision medicine is revolutionizing health care, particularly by addressing patient variability due to different biological profiles. As traditional treatments may not always be appropriate for certain patient subsets, the rise of biomarker-stratified clinical trials has driven the need for innovative methods. We introduced a Bayesian sequential scheme to evaluate therapeutic interventions in an intensive care unit setting, focusing on complex endpoints characterized by an excess of zeros and right truncation. By using a zero-inflated truncated Poisson model, we efficiently addressed this data complexity. The posterior distribution of rankings and the surface under the cumulative ranking curve (SUCRA) approach provided a comprehensive ranking of the subgroups studied. Different subsets of subgroups were evaluated depending on the availability of biomarker data. Interim analyses, accounting for early stopping for efficacy, were an integral aspect of our design. The simulation study demonstrated a high proportion of correct identification of the subgroup which is the most predictive of the treatment effect, as well as satisfactory false positive and true positive rates. As the role of personalized medicine grows, especially in the intensive care setting, it is critical to have designs that can manage complicated endpoints and that can control for decision error. Our method seems promising in this challenging context.
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Affiliation(s)
- Valentin Vinnat
- ECSTRRA Team, INSERM U1153, Université Paris Cité, 75010 Paris, France;
| | - Djillali Annane
- Intensive Care Unit, Raymond Poincaré Hospital, 78266 Garches, France;
| | - Sylvie Chevret
- ECSTRRA Team, INSERM U1153, Université Paris Cité, 75010 Paris, France;
- Institut Universitaire de France (IUF), 75231 Paris, France
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21
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Zheng H, Grayling MJ, Mozgunov P, Jaki T, Wason JMS. Bayesian sample size determination in basket trials borrowing information between subsets. Biostatistics 2023; 24:1000-1016. [PMID: 35993875 DOI: 10.1093/biostatistics/kxac033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 07/22/2022] [Accepted: 07/29/2022] [Indexed: 12/31/2022] Open
Abstract
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.
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Affiliation(s)
- Haiyan Zheng
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK and Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
| | - Michael J Grayling
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
| | - Pavel Mozgunov
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, CB2 0SR, UK and University of Regensburg, 93040 Regensburg, Germany
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4AX, UK
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22
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Jiang Z, Mi G, Lin J, Lorenzato C, Ji Y. A Multi-Arm Two-Stage (MATS) design for proof-of-concept and dose optimization in early-phase oncology trials. Contemp Clin Trials 2023; 132:107278. [PMID: 37419308 DOI: 10.1016/j.cct.2023.107278] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 06/10/2023] [Accepted: 06/30/2023] [Indexed: 07/09/2023]
Abstract
The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the status quo of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas where multiple doses are evaluated thoroughly in dose ranging studies, early-phase oncology dose-finding studies are characterized by the practice of identifying a single dose, such as the maximum tolerated dose (MTD) or the recommended phase 2 dose (RP2D). Following the spirit of Project Optimus, we propose an Multi-Arm Two-Stage (MATS) design for proof-of-concept (PoC) and dose optimization that allows the evaluation of two selected doses from a dose-escalation trial. The design assesses the higher dose first across multiple indications in the first stage, and adaptively enters the second stage for an indication if the higher dose exhibits promising anti-tumor activities. In the second stage, a randomized comparison between the higher and lower doses is conducted to achieve PoC and dose optimization. A Bayesian hierarchical model governs the statistical inference and decision making by borrowing information across doses, indications, and stages. Our simulation studies show that the proposed MATS design yield desirable performance. An R Shiny application has been developed and made available at https://matsdesign.shinyapps.io/mats/.
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Affiliation(s)
- Zhenghao Jiang
- Department of Statistics, University of Chicago, 5747 South Ellis Avenue, Chicago, IL 60637, United States of America
| | - Gu Mi
- Biostatistics and Programming, Sanofi, 450 Water Street, Cambridge, MA 02141, United States of America
| | - Ji Lin
- Biostatistics and Programming, Sanofi, 450 Water Street, Cambridge, MA 02141, United States of America
| | | | - Yuan Ji
- Department of Public Health Science, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, United States of America.
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23
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Quintana M, Saville BR, Vestrucci M, Detry MA, Chibnik L, Shefner J, Berry JD, Chase M, Andrews J, Sherman AV, Yu H, Drake K, Cudkowicz M, Paganoni S, Macklin EA. Design and Statistical Innovations in a Platform Trial for Amyotrophic Lateral Sclerosis. Ann Neurol 2023; 94:547-560. [PMID: 37245090 DOI: 10.1002/ana.26714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/12/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023]
Abstract
Platform trials allow efficient evaluation of multiple interventions for a specific disease. The HEALEY ALS Platform Trial is testing multiple investigational products in parallel and sequentially in persons with amyotrophic lateral sclerosis (ALS) with the goal of rapidly identifying novel treatments to slow disease progression. Platform trials have considerable operational and statistical efficiencies compared with typical randomized controlled trials due to their use of shared infrastructure and shared control data. We describe the statistical approaches required to achieve the objectives of a platform trial in the context of ALS. This includes following regulatory guidance for the disease area of interest and accounting for potential differences in outcomes of participants within the shared control (potentially due to differences in time of randomization, mode of administration, and eligibility criteria). Within the HEALEY ALS Platform Trial, the complex statistical objectives are met using a Bayesian shared parameter analysis of function and survival. This analysis serves to provide a common integrated estimate of treatment benefit, overall slowing in disease progression, as measured by function and survival while accounting for potential differences in the shared control group using Bayesian hierarchical modeling. Clinical trial simulation is used to provide a better understanding of this novel analysis method and complex design. ANN NEUROL 2023;94:547-560.
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Affiliation(s)
| | - Benjamin R Saville
- Berry Consultants, Austin, Texas, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | | | - Lori Chibnik
- Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeremy Shefner
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - James D Berry
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Marianne Chase
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Jinsy Andrews
- Neurological Institute of New York, Columbia University, New York, New York, USA
| | - Alexander V Sherman
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Hong Yu
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kristin Drake
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Merit Cudkowicz
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Sabrina Paganoni
- Sean M. Healey & AMG Center for ALS at Mass General, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Eric A Macklin
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
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24
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Harari O, Soltanifar M, Verhoek A, Heeg B. Alone, together: On the benefits of Bayesian borrowing in a meta-analytic setting. Pharm Stat 2023; 22:903-920. [PMID: 37321565 DOI: 10.1002/pst.2318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 04/11/2023] [Accepted: 05/26/2023] [Indexed: 06/17/2023]
Abstract
It is common practice to use hierarchical Bayesian model for the informing of a pediatric randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction parameter (BFP). This implicitly assumes that the BFP is intuitive and corresponds to the degree of similarity between the populations. Generalizing this model to any K ≥ 1 historical studies, naturally leads to empirical Bayes meta-analysis. In this paper we calculate the Bayesian BFPs and study the factors that drive them. We prove that simultaneous mean squared error reduction relative to an uninformed model is always achievable through application of this model. Power and sample size calculations for a future RCT, designed to be informed by multiple external RCTs, are also provided. Potential applications include inference on treatment efficacy from independent trials involving either heterogeneous patient populations or different therapies from a common class.
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Affiliation(s)
- Ofir Harari
- Real World and Advanced Analytics, Cytel Inc., Vancouver, British Columbia, Canada
- Core Clinical Sciences, Vancouver, British Columbia, Canada
| | - Mohsen Soltanifar
- Real World and Advanced Analytics, Cytel Inc., Vancouver, British Columbia, Canada
- Analytics Division, College of Professional Studies, Northeastern University, Vancouver, British Columbia, Canada
| | | | - Bart Heeg
- RWA & HEOR, Cytel Inc., Rotterdam, The Netherlands
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25
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Blay JY, Chevret S, Le Cesne A, Brahmi M, Penel N, Cousin S, Bertucci F, Bompas E, Ryckewaert T, Soibinet P, Boudou-Rouquette P, Saada Bouzid E, Soulie P, Valentin T, Lotz JP, Tosi D, Neviere Z, Cancel M, Ray-Coquard I, Gambotti L, Legrand F, Lamrani-Ghaouti A, Simon C, Even C, Massard C. Pembrolizumab in patients with rare and ultra-rare sarcomas (AcSé Pembrolizumab): analysis of a subgroup from a non-randomised, open-label, phase 2, basket trial. Lancet Oncol 2023; 24:892-902. [PMID: 37429302 DOI: 10.1016/s1470-2045(23)00282-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/25/2023] [Accepted: 06/08/2023] [Indexed: 07/12/2023]
Abstract
BACKGROUND Sarcoma is a heterogeneous group of diseases with few treatment options. Immunotherapy has shown little activity in studies including unselected sarcomas, but immune checkpoint blockers have shown activity in specific histotypes. We evaluated the activity of pembrolizumab in rare and ultra-rare sarcomas. METHODS AcSé Pembrolizumab is an ongoing phase 2, basket, multitumour study investigating the activity of pembrolizumab monotherapy in rare cancers. Here, we report the results obtained in patients with selected histotypes of rare sarcomas (incidence of less than one case per 1 000 000 people per year) recruited at 24 French hospitals. Key inclusion criteria were age 15 years or older, Eastern Cooperative Oncology Group performance status of 0-1, and advanced disease that was untreated and resistant to treatment. Patients were given pembrolizumab 200 mg intravenously on day 1 of every 21-day cycle for a maximum of 24 months. The primary endpoint was objective response rate at week 12 using Response Evaluation Criteria in Solid Tumours version 1.1, assessed by local investigators. The primary endpoint and safety were analysed in the intention-to-treat population. The AcSé Pembrolizumab study is registered with ClinicalTrials.gov, NCT03012620. FINDINGS Between Sept 4, 2017, and Dec 29, 2020, 98 patients were enrolled, of whom 97 received treatment and were included in analyses (median age 51 years [IQR 35-65]; 53 [55%] were male; 44 [45%] were female; no data were collected on race or ethnicity). 34 (35%) patients had chordomas, 14 (14%) had alveolar soft part sarcomas, 12 (12%) had SMARCA4-deficient sarcomas or malignant rhabdoid tumours, eight (8%) had desmoplastic small round cell tumours, six (6%) had epithelioid sarcomas, four (4%) had dendritic cell sarcomas, three (3%) each had clear cell sarcomas, solitary fibrous tumours, and myxoid liposarcomas, and ten (10%) had other ultra-rare histotypes. As of data cutoff (April 11, 2022), median follow-up was 13·1 months (range 0·1-52·8; IQR 4·3-19·7). At week 12, objective response rate was 6·2% (95% CI 2·3-13·0), with no complete responses and six partial responses in the 97 patients. The most common grade 3-4 adverse events were anaemia (eight [8%] of 97), alanine aminotransferase and aspartate aminotransferase increase (six [6%]), and dyspnoea (five [5%]). 86 serious adverse events were reported in 37 patients. Five deaths due to adverse events were reported, none of which were determined to be related to treatment (two due to disease progression, two due to cancer, and one due to unknown cause). INTERPRETATION Our data show the activity and manageable toxicity of pembrolizumab in some rare and ultra-rare sarcoma histotypes, and support the PD-1/PD-L1 pathway as a potential therapeutic target in selected histotypes. The completion of the basket study will provide further evidence regarding the activity and toxicity of pembrolizumab in identified rare types of cancer. FUNDING The Ligue contre le cancer, INCa, MSD. TRANSLATION For the French translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Jean-Yves Blay
- Centre Léon Bérard & Université Claude Bernard Lyon 1, Lyon, France.
| | - Sylvie Chevret
- Service de Biostatistique, Hôpital Saint Louis (AP-HP), Université Paris Cité, Paris, France
| | - Axel Le Cesne
- Gustave Roussy, Cancer Campus, Grand Paris, Villejuif, France
| | - Mehdi Brahmi
- Centre Léon Bérard & Université Claude Bernard Lyon 1, Lyon, France
| | | | | | | | - Emmanuelle Bompas
- Institut de Cancérologie de l'Ouest, Centre René Gauducheau, Nantes, France
| | | | | | | | | | - Patrick Soulie
- Institut de Cancérologie de l'Ouest, Centre Paul Papin, Angers, France
| | | | | | - Diego Tosi
- Institut Régional du Cancer de Montpellier, Centre Val d'Aurelle, Montpellier, France
| | | | | | | | | | | | | | | | - Caroline Even
- Gustave Roussy, Cancer Campus, Grand Paris, Villejuif, France
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26
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Yu Z, Wu L, Bunn V, Li Q, Lin J. Evolution of Phase II Oncology Trial Design: from Single Arm to Master Protocol. Ther Innov Regul Sci 2023; 57:823-838. [PMID: 36871111 DOI: 10.1007/s43441-023-00500-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 02/10/2023] [Indexed: 03/06/2023]
Abstract
The recent development of novel anticancer treatments with diverse mechanisms of action has accelerated the detection of treatment candidates tremendously. The rapidly changing drug development landscapes and the high failure rates in Phase III trials both underscore the importance of more efficient and robust phase II designs. The goals of phase II oncology studies are to explore the preliminary efficacy and toxicity of the investigational product and to inform future drug development strategies such as go/no-go decisions for phase III development, or dose/indication selection. These complex purposes of phase II oncology designs call for efficient, flexible, and easy-to-implement clinical trial designs. Therefore, innovative adaptive study designs with the potential of improving the efficiency of the study, protecting patients, and improving the quality of information gained from trials have been commonly used in Phase II oncology studies. Although the value of adaptive clinical trial methods in early phase drug development is generally well accepted, there is no comprehensive review and guidance on adaptive design methods and their best practice for phase II oncology trials. In this paper, we review the recent development and evolution of phase II oncology design, including frequentist multistage design, Bayesian continuous monitoring, master protocol design, and innovative design methods for randomized phase II studies. The practical considerations and the implementation of these complex design methods are also discussed.
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Affiliation(s)
- Ziji Yu
- , 95 Hayden Ave, Lexington, MA, 02421, USA.
- Takeda Pharmaceuticals, Lexington, USA.
| | - Liwen Wu
- Takeda Pharmaceuticals, Lexington, USA
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Garces S, Karis E, Merrill JT, Askanase AD, Kalunian K, Mo M, Milmont CE. Improving resource utilisation in SLE drug development through innovative trial design. Lupus Sci Med 2023; 10:e000890. [PMID: 37491104 PMCID: PMC10373732 DOI: 10.1136/lupus-2022-000890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 06/29/2023] [Indexed: 07/27/2023]
Abstract
SLE is a complex autoimmune disease with considerable unmet need. Numerous clinical trials designed to investigate novel therapies are actively enrolling patients straining limited resources and creating inefficiencies that increase enrolment challenges. This has motivated investigators developing novel drugs and treatment strategies to consider innovative trial designs that aim to improve the efficiency of generating evidence; these strategies propose conducting fewer trials, involving smaller numbers of patients, while maintaining scientific rigour in safety and efficacy data collection and analysis. In this review we present the design of two innovative phase IIb studies investigating efavaleukin alfa and rozibafusp alfa for the treatment of SLE which use an adaptive study design. This design was selected as a case study, investigating efavaleukin alfa, in the Food and Drug Administration's Complex Innovative Trial Design Pilot Program. The adaptive design approach includes prospectively planned modifications at predefined interim timepoints. Interim assessments of futility allow for a trial to end early when the investigational therapy is unlikely to provide meaningful treatment benefits to patients, which can release eligible patients to participate in other-potentially more promising-trials, or seek alternative treatments. Response-adaptive randomisation allows randomisation ratios to change based on accumulating data, in favour of the more efficacious dose arm(s), while the study is ongoing. Throughout the trial the placebo arm allocation ratio is maintained constant. These design elements can improve the statistical power in the estimation of treatment effect and increase the amount of safety and efficacy data collected for the optimal dose(s). Furthermore, these trials can provide the required evidence to potentially serve as one of two confirmatory trials needed for regulatory approval. This can reduce the need for multiple phase III trials, the total patient requirements, person-exposure risk, and ultimately the time and cost of investigational drug development programmes.
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Affiliation(s)
| | | | - Joan T Merrill
- Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
| | - Anca D Askanase
- Department of Rheumatology, Columbia University Irving Medical Center, New York (City), New York, USA
| | - Kenneth Kalunian
- Division of Rheumatology, Allergy and Immunology, University of California San Diego School of Medicine, San Diego, California, USA
| | - May Mo
- Amgen Inc, Thousand Oaks, California, USA
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Miyamoto S, Kuroda Y, Kanno T, Ueno A, Shiwa-Sudo N, Iwata-Yoshikawa N, Sakai Y, Nagata N, Arashiro T, Ainai A, Moriyama S, Kishida N, Watanabe S, Nojima K, Seki Y, Mizukami T, Hasegawa H, Ebihara H, Fukushi S, Takahashi Y, Maeda K, Suzuki T. Saturation time of exposure interval for cross-neutralization response to SARS-CoV-2: Implications for vaccine dose interval. iScience 2023; 26:106694. [PMID: 37124417 PMCID: PMC10114312 DOI: 10.1016/j.isci.2023.106694] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/30/2023] [Accepted: 04/13/2023] [Indexed: 05/02/2023] Open
Abstract
Evaluating the serum cross-neutralization responses after breakthrough infection with various SARS-CoV-2 variants provides valuable insight for developing variant-proof COVID-19 booster vaccines. However, fairly comparing the impact of breakthrough infections with distinct epidemic timing on cross-neutralization responses, influenced by the exposure interval between vaccination and infection, is challenging. To compare the impact of pre-Omicron to Omicron breakthrough infection, we estimated the effects on cross-neutralizing responses by the exposure interval using Bayesian hierarchical modeling. The saturation time required to generate saturated cross-neutralization responses differed by variant, with variants more antigenically distant from the ancestral strain requiring longer intervals of 2-4 months. The breadths of saturated cross-neutralization responses to Omicron lineages were comparable in pre-Omicron and Omicron breakthrough infections. Our results highlight the importance of vaccine dosage intervals of 4 months or longer, regardless of the antigenicity of the exposed antigen, to maximize the breadth of serum cross-neutralization covering SARS-CoV-2 Omicron lineages.
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Affiliation(s)
- Sho Miyamoto
- Department of Pathology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Yudai Kuroda
- Department of Veterinary Science, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Takayuki Kanno
- Department of Pathology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Akira Ueno
- Department of Pathology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Nozomi Shiwa-Sudo
- Department of Pathology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Naoko Iwata-Yoshikawa
- Department of Pathology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Yusuke Sakai
- Department of Pathology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Noriyo Nagata
- Department of Pathology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Takeshi Arashiro
- Department of Pathology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
- Center for Surveillance, Immunization, and Epidemiologic Research, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Akira Ainai
- Department of Pathology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Saya Moriyama
- Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Noriko Kishida
- Center for Influenza and Respiratory Virus Research, National Institute of Infectious Diseases, Tokyo 208-0011, Japan
| | - Shinji Watanabe
- Center for Influenza and Respiratory Virus Research, National Institute of Infectious Diseases, Tokyo 208-0011, Japan
| | - Kiyoko Nojima
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases, Tokyo 208-0011, Japan
| | - Yohei Seki
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases, Tokyo 208-0011, Japan
| | - Takuo Mizukami
- Department of Safety Research on Blood and Biological Products, National Institute of Infectious Diseases, Tokyo 208-0011, Japan
| | - Hideki Hasegawa
- Center for Influenza and Respiratory Virus Research, National Institute of Infectious Diseases, Tokyo 208-0011, Japan
| | - Hideki Ebihara
- Department of Virology I, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Shuetsu Fukushi
- Department of Virology I, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Yoshimasa Takahashi
- Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Ken Maeda
- Department of Veterinary Science, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
| | - Tadaki Suzuki
- Department of Pathology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan
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29
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Whitehead LE, Sailer O, Witham MD, Wason JMS. Bayesian borrowing for basket trials with longitudinal outcomes. Stat Med 2023. [PMID: 37120858 DOI: 10.1002/sim.9751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/28/2023] [Accepted: 04/16/2023] [Indexed: 05/02/2023]
Abstract
Basket trials are a novel clinical trial design in which a single intervention is investigated in multiple patient subgroups, or "baskets." They offer the opportunity to share information between subgroups, potentially increasing power to detect treatment effects. Basket trials offer several advantages over running a series of separate trials, including reduced sample sizes, increased efficiency, and reduced costs. Primarily, basket trials have been undertaken in Phase II oncology settings, but could be a promising design in other areas where a shared underlying biological mechanism drives different diseases. One such area is chronic aging-related diseases. However, trials in this area frequently have longitudinal outcomes, and therefore suitable methods are needed to share information in this setting. In this paper, we extend three Bayesian borrowing methods for a basket design with continuous longitudinal endpoints. We demonstrate our methods on a real-world dataset and in a simulation study where the aim is to detect positive basketwise treatment effects. Methods are compared with standalone analysis of each basket without borrowing. Our results confirm that methods that share information can improve power to detect positive treatment effects and increase precision over independent analysis in many scenarios. In highly heterogeneous scenarios, there is a trade-off between increased power and increased risk of type I errors. Our proposed methods for basket trials with continuous longitudinal outcomes aim to facilitate their applicability in the area of aging related diseases. Choice of method should be made based on trial priorities and the expected basketwise distribution of treatment effects.
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Affiliation(s)
- Lou E Whitehead
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Oliver Sailer
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Miles D Witham
- AGE Research Group, NIHR Newcastle Biomedical Research Centre, Newcastle University and Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, UK
| | - James M S Wason
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
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30
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Chen X, Zhang J, Jiang L, Yan F. IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy. BMC Med Res Methodol 2023; 23:66. [PMID: 36941537 PMCID: PMC10026491 DOI: 10.1186/s12874-023-01877-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/24/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Combination therapies directed at multiple targets have potentially improved treatment effects for cancer patients. Compared to monotherapy, targeted combination therapy leads to an increasing number of subgroups and complicated biomarker-based efficacy profiles, making it more difficult for efficacy evaluation in clinical trials. Therefore, it is necessary to develop innovative clinical trial designs to explore the efficacy of targeted combination therapy in different subgroups and identify patients who are more likely to benefit from the investigational combination therapy. METHODS We propose a statistical tool called 'IBIS' to Identify BIomarker-based Subgroups and apply it to the enrichment design framework. The IBIS contains three main elements: subgroup division, efficacy evaluation and subgroup identification. We first enumerate all possible subgroup divisions based on biomarker levels. Then, Jensen-Shannon divergence is used to distinguish high-efficacy and low-efficacy subgroups, and Bayesian hierarchical model (BHM) is employed to borrow information within these two subsets for efficacy evaluation. Regarding subgroup identification, a hypothesis testing framework based on Bayes factors is constructed. This framework also plays a key role in go/no-go decisions and enriching specific population. Simulation studies are conducted to evaluate the proposed method. RESULTS The accuracy and precision of IBIS could reach a desired level in terms of estimation performance. In regard to subgroup identification and population enrichment, the proposed IBIS has superior and robust characteristics compared with traditional methods. An example of how to obtain design parameters for an adaptive enrichment design under the IBIS framework is also provided. CONCLUSIONS IBIS has the potential to be a useful tool for biomarker-based subgroup identification and population enrichment in clinical trials of targeted combination therapy.
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Affiliation(s)
- Xin Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Liyun Jiang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China.
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31
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Zhao Y, Li D, Liu R, Yuan Y. Bayesian optimal phase II designs with dual-criterion decision making. Pharm Stat 2023. [PMID: 36871961 DOI: 10.1002/pst.2296] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 11/29/2022] [Accepted: 02/06/2023] [Indexed: 03/07/2023]
Abstract
The conventional phase II trial design paradigm is to make the go/no-go decision based on the hypothesis testing framework. Statistical significance itself alone, however, may not be sufficient to establish that the drug is clinically effective enough to warrant confirmatory phase III trials. We propose the Bayesian optimal phase II trial design with dual-criterion decision making (BOP2-DC), which incorporates both statistical significance and clinical relevance into decision making. Based on the posterior probability that the treatment effect reaches the lower reference value (statistical significance) and the clinically meaningful value (clinical significance), BOP2-DC allows for go/consider/no-go decisions, rather than a binary go/no-go decision. BOP2-DC is highly flexible and accommodates various types of endpoints, including binary, continuous, time-to-event, multiple, and coprimary endpoints, in single-arm and randomized trials. The decision rule of BOP2-DC is optimized to maximize the probability of a go decision when the treatment is effective or minimize the expected sample size when the treatment is futile. Simulation studies show that the BOP2-DC design yields desirable operating characteristics. The software to implement BOP2-DC is freely available at www.trialdesign.org.
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Affiliation(s)
- Yujie Zhao
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Daniel Li
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Berkeley Heights, New Jersey, USA
| | - Rong Liu
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Berkeley Heights, New Jersey, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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32
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Jiang L, Nie L, Yuan Y. Elastic priors to dynamically borrow information from historical data in clinical trials. Biometrics 2023; 79:49-60. [PMID: 34437714 DOI: 10.1111/biom.13551] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 08/02/2021] [Accepted: 08/10/2021] [Indexed: 11/30/2022]
Abstract
Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is to effectively borrow information from historical data while maintaining a reasonable type I error and minimal bias. We propose the elastic prior approach to address this challenge. Unlike existing approaches, this approach proactively controls the behavior of information borrowing and type I errors by incorporating a well-known concept of clinically significant difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of prespecified criteria such that the resulting prior will strongly borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. The elastic prior approach has a desirable property of being information borrowing consistent, that is, asymptotically controls type I error at the nominal value, no matter that historical data are congruent or not to the trial data. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power. The proposed approach is applicable to binary, continuous, and survival endpoints.
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Affiliation(s)
- Liyun Jiang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lei Nie
- Center for Drug Evaluation and Research, Food and Drug Administration (FDA), Silver Spring, Maryland, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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33
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Chen X, Zhang J, Jiang L, Yan F. Shotgun-2: A Bayesian phase I/II basket trial design to identify indication-specific optimal biological doses. Stat Methods Med Res 2023; 32:443-464. [PMID: 36217826 DOI: 10.1177/09622802221129049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named "shotgun-2," to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional "phase I + cohort expansion" design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.
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Affiliation(s)
- Xin Chen
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
| | - Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
| | - Liyun Jiang
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, 56651China Pharmaceutical University, Nanjing, China
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34
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Chen C, Hsiao CF. Bayesian hierarchical models for adaptive basket trial designs. Pharm Stat 2023; 22:531-546. [PMID: 36625301 DOI: 10.1002/pst.2289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 10/12/2022] [Accepted: 12/18/2022] [Indexed: 01/11/2023]
Abstract
Basket trials evaluate a single drug targeting a single genetic variant in multiple cancer cohorts. Empirical findings suggest that treatment efficacy across baskets may be heterogeneous. Most modern basket trial designs use Bayesian methods. These methods require the prior specification of at least one parameter that permits information sharing across baskets. In this study, we provide recommendations for selecting a prior for scale parameters for adaptive basket trials by using Bayesian hierarchical modeling. Heterogeneity among baskets attracts much attention in basket trial research, and substantial heterogeneity challenges the basic assumption of exchangeability of Bayesian hierarchical approach. Thus, we also allowed each stratum-specific parameter to be exchangeable or nonexchangeable with similar strata by using data observed in an interim analysis. Through a simulation study, we evaluated the overall performance of our design based on statistical power and type I error rates. Our research contributes to the understanding of the properties of Bayesian basket trial designs.
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Affiliation(s)
- Chian Chen
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Chin-Fu Hsiao
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
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35
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Liu S, Takeda K, Rong A. An adaptive biomarker basket design in phase II oncology trials. Pharm Stat 2023; 22:128-142. [PMID: 36163614 DOI: 10.1002/pst.2264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 07/30/2022] [Accepted: 09/09/2022] [Indexed: 02/01/2023]
Abstract
The phase II basket trial in oncology is a novel design that enables the simultaneous assessment of treatment effects of one anti-cancer targeted agent in multiple cancer types. Biomarkers could potentially associate with the clinical outcomes and re-define clinically meaningful treatment effects. It is therefore natural to develop a biomarker-based basket design to allow the prospective enrichment of the trials with the adaptive selection of the biomarker-positive (BM+) subjects who are most sensitive to the experimental treatment. We propose a two-stage phase II adaptive biomarker basket (ABB) design based on a potential predictive biomarker measured on a continuous scale. At Stage 1, the design incorporates a biomarker cutoff estimation procedure via a hierarchical Bayesian model with biomarker as a covariate (HBMbc). At Stage 2, the design enrolls only BM+ subjects, defined as those with the biomarker values exceeding the biomarker cutoff within each cancer type, and subsequently assesses the early efficacy and/or futility stopping through the pre-defined interim analyses. At the end of the trial, the response rate of all BM+ subjects for each cancer type can guide drug development, while the data from all subjects can be used to further model the relationship between the biomarker value and the clinical outcome for potential future research. The extensive simulation studies show that the ABB design could produce a good estimate of the biomarker cutoff to select BM+ subjects with high accuracy and could outperform the existing phase II basket biomarker cutoff design under various scenarios.
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Affiliation(s)
- Shufang Liu
- Data Science, Astellas Pharma Inc., Northbrook, Illinois, USA
| | - Kentaro Takeda
- Data Science, Astellas Pharma Inc., Northbrook, Illinois, USA
| | - Alan Rong
- Data Science, Astellas Pharma Inc., Northbrook, Illinois, USA
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36
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Yoo W, Kim S, Garcia M, Mehta S, Sanai N. Evaluation of two-stage designs of Phase 2 single-arm trials in glioblastoma: a systematic review. BMC Med Res Methodol 2022; 22:327. [PMID: 36550391 PMCID: PMC9773486 DOI: 10.1186/s12874-022-01810-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Due to economical and ethical reasons, the two-stage designs have been widely used for Phase 2 single-arm trials in oncology because the designs allow us to stop the trial early if the proposed treatment is likely to be ineffective. Nonetheless, none has examined the usage for published articles that had applied the two-stage designs in Phase 2 single-arm trials in brain tumor. A complete systematic review and discussions for overcoming design issues might be important to better understand why oncology trials have shown low success rates in early phase trials. METHODS We systematically reviewed published single-arm two-stage Phase 2 trials for patients with glioblastoma and high-grade gliomas (including newly diagnosed or recurrent). We also sought to understand how these two-stage trials have been implemented and discussed potential design issues which we hope will be helpful for investigators who work with Phase 2 clinical trials in rare and high-risk cancer studies including Neuro-Oncology. The systematic review was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)-statement. Searches were conducted using the electronic database of PubMed, Google Scholar and ClinicalTrials.gov for potentially eligible publications from inception by two independent researchers up to May 26, 2022. The followings were key words for the literature search as index terms or free-text words: "phase II trials", "glioblastoma", and "two-stage design". We extracted disease type and setting, population, therapeutic drug, primary endpoint, input parameters and sample size results from two-stage designs, and historical control reference, and study termination status. RESULTS Among examined 29 trials, 12 trials (41%) appropriately provided key input parameters and sample size results from two-stage design implementation. Among appropriately implemented 12 trials, discouragingly only 3 trials (10%) explained the reference information of historical control rates. Most trials (90%) used Simon's two-stage designs. Only three studies have been completed for both stages and two out of the three completed studies had shown the efficacy. CONCLUSIONS Right implementation for two-stage design and sample size calculation, transparency of historical control and experimental rates, appropriate selection on primary endpoint, potential incorporation of adaptive designs, and utilization of Phase 0 paradigm might help overcoming the challenges on glioblastoma therapeutic trials in Phase 2 trials.
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Affiliation(s)
- Wonsuk Yoo
- grid.427785.b0000 0001 0664 3531Ivy Brain Tumor Center, Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ 85013 USA
| | - Seongho Kim
- grid.254444.70000 0001 1456 7807Karmanos Cancer Institute, Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201 USA
| | - Michael Garcia
- grid.427785.b0000 0001 0664 3531Department of Radiation Oncology, Barrow Neurological Institute, Phoenix, AZ 85013 USA
| | - Shwetal Mehta
- grid.427785.b0000 0001 0664 3531Ivy Brain Tumor Center, Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ 85013 USA
| | - Nader Sanai
- grid.427785.b0000 0001 0664 3531Ivy Brain Tumor Center, Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ 85013 USA
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37
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Ouma LO, Grayling MJ, Wason JMS, Zheng H. Bayesian modelling strategies for borrowing of information in randomised basket trials. J R Stat Soc Ser C Appl Stat 2022; 71:2014-2037. [PMID: 36636028 PMCID: PMC9827857 DOI: 10.1111/rssc.12602] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 09/01/2022] [Indexed: 02/01/2023]
Abstract
Basket trials are an innovative precision medicine clinical trial design evaluating a single targeted therapy across multiple diseases that share a common characteristic. To date, most basket trials have been conducted in early-phase oncology settings, for which several Bayesian methods permitting information sharing across subtrials have been proposed. With the increasing interest of implementing randomised basket trials, information borrowing could be exploited in two ways; considering the commensurability of either the treatment effects or the outcomes specific to each of the treatment groups between the subtrials. In this article, we extend a previous analysis model based on distributional discrepancy for borrowing over the subtrial treatment effects ('treatment effect borrowing', TEB) to borrowing over the subtrial groupwise responses ('treatment response borrowing', TRB). Simulation results demonstrate that both modelling strategies provide substantial gains over an approach with no borrowing. TRB outperforms TEB especially when subtrial sample sizes are small on all operational characteristics, while the latter has considerable gains in performance over TRB when subtrial sample sizes are large, or the treatment effects and groupwise mean responses are noticeably heterogeneous across subtrials. Further, we notice that TRB, and TEB can potentially lead to different conclusions in the analysis of real data.
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Affiliation(s)
- Luke O. Ouma
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
| | - Michael J. Grayling
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
| | - James M. S. Wason
- Population Health Sciences InstituteNewcastle UniversityNewcastle upon TyneUK
| | - Haiyan Zheng
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
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38
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Innovations in Clinical Development in Rare Diseases of Children and Adults: Small Populations and/or Small Patients. Paediatr Drugs 2022; 24:657-669. [PMID: 36241954 DOI: 10.1007/s40272-022-00538-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/11/2022] [Indexed: 10/17/2022]
Abstract
Many of the afflictions of children are rare diseases. This creates numerous drug development challenges related to small populations, including limited information about the disease state, enrollment challenges, and diminished incentives for pediatric development of novel therapies by pharmaceutical and biotechnology sponsors. We review selected innovations in clinical development that may partially mitigate some of these difficulties, starting with the concept of development efficiency for individual clinical trials, clinical programs (involving multiple trials for a single drug), and clinical portfolios of multiple drugs, and decision analysis as a tool to optimize efficiency. Development efficiency is defined as the ability to reach equally rigorous or more rigorous conclusions in less time, with fewer trial participants, or with fewer resources. We go on to discuss efficient methods for matching targeted therapies to biomarker-defined subgroups, methods for eliminating or reducing the need for natural history data to guide rare disease development, the use of basket trials to enhance efficiency by grouping multiple similar disease applications in a single clinical trial, and the use of alternative data sources including historical controls to augment or replace concurrent controls in clinical studies. Greater understanding and broader application of these methods could lead to improved therapies and/or more widespread and rapid access to novel therapies for rare diseases in both children and adults.
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39
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Ouma LO, Wason JMS, Zheng H, Wilson N, Grayling M. Design and analysis of umbrella trials: Where do we stand? Front Med (Lausanne) 2022; 9:1037439. [PMID: 36313987 PMCID: PMC9596938 DOI: 10.3389/fmed.2022.1037439] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention. Methods We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology. Findings We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse. Conclusions Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.
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Affiliation(s)
- Luke O. Ouma
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - James M. S. Wason
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Haiyan Zheng
- Medical Research Council (MRC) Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Nina Wilson
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Michael Grayling
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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Liu Y, Kane M, Esserman D, Blaha O, Zelterman D, Wei W. Bayesian local exchangeability design for phase II basket trials. Stat Med 2022; 41:4367-4384. [PMID: 35777367 DOI: 10.1002/sim.9514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 04/27/2022] [Accepted: 06/19/2022] [Indexed: 11/08/2022]
Abstract
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.
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Affiliation(s)
- Yilin Liu
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Michael Kane
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Denise Esserman
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Ondrej Blaha
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Daniel Zelterman
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Wei Wei
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
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Pan J, Bunn V, Hupf B, Lin J. Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses. J Biopharm Stat 2022; 32:613-626. [PMID: 35737650 DOI: 10.1080/10543406.2022.2089160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
It is crucial in clinical trials to investigate treatment effect consistency across subgroups defined by patient baseline characteristics. However, there may be treatment effect variability across subgroups due to small subgroup sample size. Various Bayesian models have been proposed to incorporate this variability when borrowing information across subgroups. These models rely on the underlying assumption that patients with similar characteristics will have similar outcomes to the same treatment. Patient populations within each subgroup must subjectively be deemed similar enough Pocock (1976) to borrow response information across subgroups. We propose utilizing the machine learning method of Bayesian Additive Regression Trees (BART) to provide a method for subgroup borrowing that does not rely on an underlying assumption of homogeneity between subgroups. BART is a data-driven approach that utilizes patient-level observations. The amount of borrowing between subgroups automatically adjusts as BART learns the covariate-response relationships. Modeling patient-level data rather than treating the subgroup as a single unit minimizes assumptions regarding homogeneity across subgroups. We illustrate the use of BART in this context by comparing performance from existing subgroup borrowing methods in a simulation study and a case study in non-small cell lung cancer. The application of BART in the context of subgroup analyses alleviates the need to subjectively choose how much information to borrow based on subgroup similarity. Having the amount of borrowing be analytically determined and controlled for based on the similarity of individual patient-level characteristics allows for more objective decision making in the drug development process with many other applications including basket trials.
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Affiliation(s)
- Jane Pan
- Department of Biostatistics, University of California, Los Angeles, California, USA
| | - Veronica Bunn
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Bradley Hupf
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Jianchang Lin
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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He T, Liu R, Liu M, Lin J. PMED: Optimal Bayesian Platform Trial Design with Multiple Endpoints. J Biopharm Stat 2022; 32:567-581. [DOI: 10.1080/10543406.2022.2080692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Tian He
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA
| | - Rachael Liu
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Meizi Liu
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Jianchang Lin
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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Rousseau B, Bieche I, Pasmant E, Hamzaoui N, Leulliot N, Michon L, de Reynies A, Attignon V, Foote MB, Masliah-Planchon J, Svrcek M, Cohen R, Simmet V, Augereau P, Malka D, Hollebecque A, Pouessel D, Gomez-Roca C, Guimbaud R, Bruyas A, Guillet M, Grob JJ, Duluc M, Cousin S, de la Fouchardiere C, Flechon A, Rolland F, Hiret S, Saada-Bouzid E, Bouche O, Andre T, Pannier D, El Hajbi F, Oudard S, Tournigand C, Soria JC, Champiat S, Gerber DG, Stephens D, Lamendola-Essel MF, Maron SB, Diplas BH, Argiles G, Krishnan AR, Tabone-Eglinger S, Ferrari A, Segal NH, Cercek A, Hoog-Labouret N, Legrand F, Simon C, Lamrani-Ghaouti A, Diaz LA, Saintigny P, Chevret S, Marabelle A. PD-1 Blockade in Solid Tumors with Defects in Polymerase Epsilon. Cancer Discov 2022; 12:1435-1448. [PMID: 35398880 PMCID: PMC9167784 DOI: 10.1158/2159-8290.cd-21-0521] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 03/09/2022] [Accepted: 04/04/2022] [Indexed: 11/16/2022]
Abstract
Missense mutations in the polymerase epsilon (POLE) gene have been reported to generate proofreading defects resulting in an ultramutated genome and to sensitize tumors to checkpoint blockade immunotherapy. However, many POLE-mutated tumors do not respond to such treatment. To better understand the link between POLE mutation variants and response to immunotherapy, we prospectively assessed the efficacy of nivolumab in a multicenter clinical trial in patients bearing advanced mismatch repair-proficient POLE-mutated solid tumors. We found that only tumors harboring selective POLE pathogenic mutations in the DNA binding or catalytic site of the exonuclease domain presented high mutational burden with a specific single-base substitution signature, high T-cell infiltrates, and a high response rate to anti-PD-1 monotherapy. This study illustrates how specific DNA repair defects sensitize to immunotherapy. POLE proofreading deficiency represents a novel agnostic biomarker for response to PD-1 checkpoint blockade therapy. SIGNIFICANCE POLE proofreading deficiency leads to high tumor mutational burden with high tumor-infiltrating lymphocytes and predicts anti-PD-1 efficacy in mismatch repair-proficient tumors. Conversely, tumors harboring POLE mutations not affecting proofreading derived no benefit from PD-1 blockade. POLE proofreading deficiency is a new tissue-agnostic biomarker for cancer immunotherapy. This article is highlighted in the In This Issue feature, p. 1397.
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Affiliation(s)
- Benoit Rousseau
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ivan Bieche
- Department of Genetics, Institut Curie, Paris, France
- Institut Cochin, Inserm U1016, CNRS UMR8104, Université de Paris, CARPEM, Paris, France
| | - Eric Pasmant
- Institut Cochin, Inserm U1016, CNRS UMR8104, Université de Paris, CARPEM, Paris, France
- Fédération de Génétique et Médecine Génomique, Hôpital Cochin, AP-HP.Centre-Université de Paris, Paris, France
| | - Nadim Hamzaoui
- Institut Cochin, Inserm U1016, CNRS UMR8104, Université de Paris, CARPEM, Paris, France
- Fédération de Génétique et Médecine Génomique, Hôpital Cochin, AP-HP.Centre-Université de Paris, Paris, France
| | - Nicolas Leulliot
- Cibles Thérapeutiques et Conception de Médicaments, CNRS UMR8015, Université de Paris, UFR de Pharmacie de Paris, Paris, France
| | - Lucas Michon
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, Lyon, France
| | - Aurelien de Reynies
- Université de Paris, Centre de Recherche des Cordeliers, UMRS1138, AP-HP, SeqOIA-IT, Paris, France
| | | | - Michael B. Foote
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Magali Svrcek
- Pathology department, Saint Antoine Hospital
- Sorbonne Université, INSERM, Unité Mixte de Recherche Scientifique 938 and SIRIC CURAMUS, Centre de Recherche Saint-Antoine, Equipe Instabilité des Microsatellites et Cancer, Equipe labellisée par la Ligue Nationale contre le Cancer, F-75012 Paris, France
| | - Romain Cohen
- Sorbonne Université, INSERM, Unité Mixte de Recherche Scientifique 938 and SIRIC CURAMUS, Centre de Recherche Saint-Antoine, Equipe Instabilité des Microsatellites et Cancer, Equipe labellisée par la Ligue Nationale contre le Cancer, F-75012 Paris, France
- Medical Oncology Department, Hôpital Saint-Antoine, Paris, France
| | - Victor Simmet
- Department of Medical Oncology, Institut de Cancérologie de l’Ouest (ICO), Angers, France
| | - Paule Augereau
- Department of Medical Oncology, Institut de Cancérologie de l’Ouest (ICO), Angers, France
| | - David Malka
- Département d’Innovation Thérapeutique et d’Essais Précoces (DITEP), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Antoine Hollebecque
- Département d’Innovation Thérapeutique et d’Essais Précoces (DITEP), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Damien Pouessel
- Department of Medical Oncology, Institut Claudius Regaud / IUCT Oncopole, Toulouse, France
| | - Carlos Gomez-Roca
- Department of Medical Oncology, Institut Claudius Regaud / IUCT Oncopole, Toulouse, France
| | | | - Amandine Bruyas
- Department of Medical Oncology, Hôpital de la Croix-Rousse, Lyon, France
| | - Marielle Guillet
- Department of Gastroenterology and Digestive Oncology, Hôpital de la Croix-Rousse, Lyon, France
| | | | - Muriel Duluc
- Dermatology and Oncology, Hôpital de la Timone, Marseille, France
| | | | | | - Aude Flechon
- Department of medical Oncology, Centre Leon Berard, Lyon, France
| | - Frederic Rolland
- Department of Medical Oncology, ICO Institut de Cancerologie de l’Ouest René Gauducheau, Saint-Herblain, France
| | - Sandrine Hiret
- Department of Medical Oncology, ICO Institut de Cancerologie de l’Ouest René Gauducheau, Saint-Herblain, France
| | - Esma Saada-Bouzid
- Medical Oncology, Centre Anticancer Antoine Lacassagne, Nice, France
| | - Olivier Bouche
- Gastroenterology and Digestive Oncology, CHU de Reims - Hôpital Robert Debré, Reims, France
| | - Thierry Andre
- Medical Oncology Department, Hôpital Saint-Antoine, Paris, France
| | | | | | - Stephane Oudard
- Oncology, Hopital Europeen Georges Pompidou, AP-HP, Paris, France
| | | | - Jean-Charles Soria
- Département d’Innovation Thérapeutique et d’Essais Précoces (DITEP), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Stephane Champiat
- Département d’Innovation Thérapeutique et d’Essais Précoces (DITEP), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Drew G. Gerber
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dennis Stephens
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Steven B. Maron
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bill H. Diplas
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Guillem Argiles
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Asha R. Krishnan
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Anthony Ferrari
- Platform of Bioinformatics Gilles Thomas-Synergie Lyon Cancer, Centre Léon Bérard, Lyon
| | - Neil H. Segal
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrea Cercek
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Frederic Legrand
- Research and Innovation, Institut National du Cancer, Boulogne-Billancourt, France
| | | | | | - Luis A. Diaz
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pierre Saintigny
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, Lyon, France
- Department of medical Oncology, Centre Leon Berard, Lyon, France
| | | | - Aurelien Marabelle
- Département d’Innovation Thérapeutique et d’Essais Précoces (DITEP), Gustave Roussy, Université Paris Saclay, Villejuif, France
- U1015 & CIC1428, Institut national de la santé et de la recherche médicale (INSERM), Villejuif, France
- Faculté de Médecine, Université Paris Saclay, Le Kremlin-Bicetre, France
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Baumann L, Krisam J, Kieser M. Monotonicity conditions for avoiding counterintuitive decisions in basket trials. Biom J 2022; 64:934-947. [PMID: 35692061 DOI: 10.1002/bimj.202100287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 02/11/2022] [Accepted: 03/05/2022] [Indexed: 11/10/2022]
Abstract
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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Affiliation(s)
- Lukas Baumann
- Institute of Medical Biometry, University of Heidelberg, Heidelberg, Germany
| | - Johannes Krisam
- Institute of Medical Biometry, University of Heidelberg, Heidelberg, Germany
| | - Meinhard Kieser
- Institute of Medical Biometry, University of Heidelberg, Heidelberg, Germany
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Zhao Y, Tang RS, Du Y, Yuan Y. A bayesian platform trial design to simultaneously evaluate multiple drugs in multiple indications with mixed endpoints. Biometrics 2022. [PMID: 35546501 DOI: 10.1111/biom.13694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 04/22/2022] [Indexed: 11/29/2022]
Abstract
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 (e.g., 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. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yujie Zhao
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States
| | - Rui Sammi Tang
- Servier Pharmaceuticals, Boston, MA, 02210, United States
| | - Yeting Du
- Servier Pharmaceuticals, Boston, MA, 02210, United States
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States
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He L, Ren Y, Chen H, Guinn D, Parashar D, Chen C, Yuan SS, Korostyshevskiy V, Beckman RA. Efficiency of a randomized confirmatory basket trial design constrained to control the family wise error rate by indication. Stat Methods Med Res 2022; 31:1207-1223. [DOI: 10.1177/09622802221091901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Basket trials pool histologic indications sharing molecular pathophysiology, improving development efficiency. Currently, basket trials have been confirmatory only for exceptional therapies. Our previous randomized basket design may be generally suitable in the resource-intensive confirmatory phase, maintains high power even with modest effect sizes, and provides nearly k-fold increased efficiency for k indications, but controls false positives for the pooled result only. Since family wise error rate by indications may sometimes be required, we now simulate a variant of this basket design controlling family wise error rate at 0.025 k, the total family wise error rate of k separate randomized trials. We simulated this modified design under numerous scenarios varying design parameters. Only designs controlling family wise error rate and minimizing estimation bias were allowable. Optimal performance results when [Formula: see text]. We report efficiency (expected # true positives/expected sample size) relative to k parallel studies, at 90% power (“uncorrected”) or at the power achieved in the basket trial (“corrected,” because conventional designs could also increase efficiency by sacrificing power). Efficiency and power (percentage active indications identified) improve with a higher percentage of initial indications active. Up to 92% uncorrected and 38% corrected efficiency improvement is possible. Even under family wise error rate control, randomized confirmatory basket trials substantially improve development efficiency. Initial indication selection is critical.
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Affiliation(s)
- Linchen He
- Department of Biostatistics, Bioinformatics and Biomathematics, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY, USA
| | - Yuru Ren
- Department of Biostatistics, Bioinformatics and Biomathematics, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Han Chen
- Department of Biostatistics, Bioinformatics and Biomathematics, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Daphne Guinn
- Program for Regulatory Science and Medicine, Georgetown University, Washington, DC, USA
- Department of Pharmacology and Physiology, Georgetown University, Washington, DC, USA
| | - Deepak Parashar
- Statistics and Epidemiology Unit & Cancer Research Centre, Warwick Medical School, University of Warwick, Coventry, UK
- The Alan Turing Institute for Data Science and Artificial Intelligence, The British Library, London, UK
| | - Cong Chen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Shuai Sammy Yuan
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, USA
- Kite Pharma, a Gilead Company, Santa Monica, CA, USA
| | - Valeriy Korostyshevskiy
- Department of Biostatistics, Bioinformatics and Biomathematics, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Robert A. Beckman
- Department of Biostatistics, Bioinformatics and Biomathematics, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
- Department of Oncology, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
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Maia IS, Kawano-Dourado L, Zampieri FG, Damiani LP, Nakagawa RH, Gurgel RM, Negrelli K, Gomes SP, Paisani D, Lima LM, Santucci EV, Valeis N, Laranjeira LN, Lewis R, Fitzgerald M, Carvalho CR, Brochard L, Cavalcanti AB. High flow nasal catheter therapy versus non-invasive positive pressure ventilation in acute respiratory failure (RENOVATE trial): protocol and statistical analysis plan. CRIT CARE RESUSC 2022; 24:61-70. [PMID: 38046839 PMCID: PMC10692619 DOI: 10.51893/2022.1.oa8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The best way to offer non-invasive respiratory support across several aetiologies of acute respiratory failure (ARF) is presently unclear. Both high flow nasal catheter (HFNC) therapy and non-invasive positive pressure ventilation (NIPPV) may improve outcomes in critically ill patients by avoiding the need for invasive mechanical ventilation (IMV). Objective: Describe the details of the protocol and statistical analysis plan designed to test whether HFNC therapy is non-inferior or even superior to NIPPV in patients with ARF due to different aetiologies. Methods: RENOVATE is a multicentre adaptive randomised controlled trial that is recruiting patients from adult emergency departments, wards and intensive care units (ICUs). It takes advantage of an adaptive Bayesian framework to assess the effectiveness of HFNC therapy versus NIPPV in four subgroups of ARF (hypoxaemic non-immunocompromised, hypoxaemic immunocompromised, chronic obstructive pulmonary disease exacerbations, and acute cardiogenic pulmonary oedema). The study will report the posterior probabilities of non-inferiority, superiority or futility for the comparison between HFNC therapy and NIPPV. The study assumes neutral priors and the final sample size is not fixed. The final sample size will be determined by a priori determined stopping rules for non-inferiority, superiority and futility for each subgroup or by reaching the maximum of 2000 patients. Outcomes: The primary endpoint is endotracheal intubation or death within 7 days. Secondary outcomes are 28-day and 90-day mortality, and ICU-free and IMV-free days in the first 28 days. Results and conclusions: RENOVATE is designed to provide evidence on whether HFNC therapy improves, compared with NIPPV, important patient-centred outcomes in different aetiologies of ARF. Here, we describe the rationale, design and status of the trial. Trial registration:ClinicalTrials.gov NCT03643939.
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Affiliation(s)
- Israel S. Maia
- HCor Research Institute, Hospital do Coracao, Sao Paulo, Brazil
- Anesthesiology Division, Medical School, University of Sao Paulo, Sao Paulo, Brazil
| | - Leticia Kawano-Dourado
- HCor Research Institute, Hospital do Coracao, Sao Paulo, Brazil
- Pulmonary Division, Medical School, University of Sao Paulo, Sao Paulo, Brazil
| | | | | | | | | | - Karina Negrelli
- HCor Research Institute, Hospital do Coracao, Sao Paulo, Brazil
| | | | - Denise Paisani
- HCor Research Institute, Hospital do Coracao, Sao Paulo, Brazil
| | - Lucas M. Lima
- HCor Research Institute, Hospital do Coracao, Sao Paulo, Brazil
| | | | - Nanci Valeis
- HCor Research Institute, Hospital do Coracao, Sao Paulo, Brazil
| | | | - Roger Lewis
- University of California, Los Angeles (UCLA), Los Angeles, California, USA
- Berry Consultants, Austin, Texas, USA
| | | | | | - Laurent Brochard
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, Canada
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada
| | - Alexandre B. Cavalcanti
- HCor Research Institute, Hospital do Coracao, Sao Paulo, Brazil
- Anesthesiology Division, Medical School, University of Sao Paulo, Sao Paulo, Brazil
| | - For the RENOVATE Investigators and the BRICNet
- HCor Research Institute, Hospital do Coracao, Sao Paulo, Brazil
- Anesthesiology Division, Medical School, University of Sao Paulo, Sao Paulo, Brazil
- Pulmonary Division, Medical School, University of Sao Paulo, Sao Paulo, Brazil
- University of California, Los Angeles (UCLA), Los Angeles, California, USA
- Berry Consultants, Austin, Texas, USA
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, Canada
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada
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Turner RM, Turkova A, Moore CL, Bamford A, Archary M, Barlow-Mosha LN, Cotton MF, Cressey TR, Kaudha E, Lugemwa A, Lyall H, Mujuru HA, Mulenga V, Musiime V, Rojo P, Tudor-Williams G, Welch SB, Gibb DM, Ford D, White IR. Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial. BMC Med Res Methodol 2022; 22:49. [PMID: 35184739 PMCID: PMC8858505 DOI: 10.1186/s12874-022-01539-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 02/09/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Clinical trial investigators may need to evaluate treatment effects in a specific subgroup (or subgroups) of participants in addition to reporting results of the entire study population. Such subgroups lack power to detect a treatment effect, but there may be strong justification for borrowing information from a larger patient group within the same trial, while allowing for differences between populations. Our aim was to develop methods for eliciting expert opinions about differences in treatment effect between patient populations, and to incorporate these opinions into a Bayesian analysis.
Methods
We used an interaction parameter to model the relationship between underlying treatment effects in two subgroups. Elicitation was used to obtain clinical opinions on the likely values of the interaction parameter, since this parameter is poorly informed by the data. Feedback was provided to experts to communicate how uncertainty about the interaction parameter corresponds with relative weights allocated to subgroups in the Bayesian analysis. The impact on the planned analysis was then determined.
Results
The methods were applied to an ongoing non-inferiority trial designed to compare antiretroviral therapy regimens in 707 children living with HIV and weighing ≥ 14 kg, with an additional group of 85 younger children weighing < 14 kg in whom the treatment effect will be estimated separately. Expert clinical opinion was elicited and demonstrated that substantial borrowing is supported. Clinical experts chose on average to allocate a relative weight of 78% (reduced from 90% based on sample size) to data from children weighing ≥ 14 kg in a Bayesian analysis of the children weighing < 14 kg. The total effective sample size in the Bayesian analysis was 386 children, providing 84% predictive power to exclude a difference of more than 10% between arms, whereas the 85 younger children weighing < 14 kg provided only 20% power in a standalone frequentist analysis.
Conclusions
Borrowing information from a larger subgroup or subgroups can facilitate estimation of treatment effects in small subgroups within a clinical trial, leading to improved power and precision. Informative prior distributions for interaction parameters are required to inform the degree of borrowing and can be informed by expert opinion. We demonstrated accessible methods for obtaining opinions.
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Zabor EC, Kane MJ, Roychoudhury S, Nie L, Hobbs BP. Bayesian basket trial design with false-discovery rate control. Clin Trials 2022; 19:297-306. [PMID: 35128970 DOI: 10.1177/17407745211073624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Recent advances in developing "tumor agnostic" oncology therapies have identified molecular targets that define patient subpopulations in a manner that supersedes conventional criteria for cancer classification. These successes have produced effective targeted therapies that are administered to patients regardless of their tumor histology. Trials have evolved as well with master protocol designs. By blending translational and clinical science, basket trials in particular are well-suited to investigate and develop targeted therapies among multiple cancer histologies. However, basket trials intrinsically involve more complex design decisions, including issues of multiple testing across baskets, and guidance for investigators is needed. METHODS The sensitivity of the multisource exchangeability model to prior specification under differing degrees of response heterogeneity is explored through simulation. Then, a multisource exchangeability model design that incorporates control of the false-discovery rate is presented and a simulation study compares the operating characteristics to a design where the family-wise error rate is controlled and to the frequentist approach of treating the baskets as independent. Simulations are based on the original design of a real-world clinical trial, the SUMMIT trial, which investigated Neratinib treatment for a variety of solid tumors. The methods studied here are specific to single-arm phase II trials with binary outcomes. RESULTS Values of prior probability of exchangeability in the multisource exchangeability model between 0.1 and 0.3 provide the best trade-offs between gain in precision and bias, especially when per-basket sample size is below 30. Application of these calibration results to a re-analysis of the SUMMIT trial showed that the breast basket exceeded the null response rate with posterior probability of 0.999 while having low posterior probability of exchangeability with all other baskets. Simulations based on the design of the SUMMIT trial revealed that there is meaningful improvement in power even in baskets with small sample size when the false-discovery rate is controlled as opposed to the family-wise error rate. For example, when only the breast basket was active, with a sample size of 25, the power was 0.76 when the false-discovery rate was controlled at 0.05 but only 0.56 when the family-wise error rate was controlled at 0.05, indicating that impractical sample sizes for the phase II setting would be needed to achieve acceptable power while controlling the family-wise error rate in this setting of a trial with 10 baskets. CONCLUSION Selection of the prior exchangeability probability based on calibration and incorporation of false-discovery rate control result in multisource exchangeability model designs with high power to detect promising treatments in the context of phase II basket trials.
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Affiliation(s)
| | | | | | - Lei Nie
- U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Brian P Hobbs
- Dell Medical School, The University of Texas at Austin, Austin, TX, USA
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Belay SY, Guo X, Lin X, Xia F, Xu J. Bayesian basket trial design accounting for multiple cutoffs of an ambiguous biomarker. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2029555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
| | - Xiang Guo
- Statistics and Data Science, BeiGene Co. Ltd., Shanghai, China
| | - Xiao Lin
- Statistics and Data Science, BeiGene Co. Ltd., Shanghai, China
| | - Fan Xia
- CSPC Phamaceutical Group Limited, Shanghai, China
| | - Jin Xu
- School of Statistics, East China Normal University, Shanghai, China
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, Shanghai, China
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