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Struebing A, McKibbon C, Ruan H, Mackay E, Dennis N, Velummailum R, He P, Tanaka Y, Xiong Y, Springford A, Rosenlund M. Augmenting external control arms using Bayesian borrowing: a case study in first-line non-small cell lung cancer. J Comp Eff Res 2024; 13:e230175. [PMID: 38573331 PMCID: PMC11036906 DOI: 10.57264/cer-2023-0175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/01/2024] [Indexed: 04/05/2024] Open
Abstract
Aim: This study aimed to improve comparative effectiveness estimates and discuss challenges encountered through the application of Bayesian borrowing (BB) methods to augment an external control arm (ECA) constructed from real-world data (RWD) using historical clinical trial data in first-line non-small-cell lung cancer (NSCLC). Materials & methods: An ECA for a randomized controlled trial (RCT) in first-line NSCLC was constructed using ConcertAI Patient360™ to assess chemotherapy with or without cetuximab, in the bevacizumab-inappropriate subpopulation. Cardinality matching was used to match patient characteristics between the treatment arm (cetuximab + chemotherapy) and ECA. Overall survival (OS) was assessed as the primary outcome using Cox proportional hazards (PH). BB was conducted using a static power prior under a Weibull PH parameterization with borrowing weights from 0.0 to 1.0 and augmentation of the ECA from a historical control trial. Results: The constructed ECA yielded a higher overall survival (OS) hazard ratio (HR) (HR = 1.53; 95% CI: 1.21-1.93) than observed in the matched population of the RCT (HR = 0.91; 95% CI: 0.73-1.13). The OS HR decreased through the incorporation of BB (HR = 1.30; 95% CI: 1.08-1.54, borrowing weight = 1.0). BB was applied to augment the RCT control arm via a historical control which improved the precision of the observed HR estimate (1.03; 95% CI: 0.86-1.22, borrowing weight = 1.0), in comparison to the matched population of the RCT alone. Conclusion: In this study, the RWD ECA was unable to successfully replicate the OS estimates from the matched population of the selected RCT. The inability to replicate could be due to unmeasured confounding and variations in time-periods, follow-up and subsequent therapy. Despite these findings, we demonstrate how BB can improve precision of comparative effectiveness estimates, potentially aid as a bias assessment tool and mitigate challenges of traditional methods when appropriate external data sources are available.
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Affiliation(s)
| | | | - Haoyao Ruan
- Cytel Inc., Toronto, Ontario, M5J, 2P1, Canada
| | - Emma Mackay
- Cytel Inc., Toronto, Ontario, M5J, 2P1, Canada
| | | | | | - Philip He
- Daiichi Sankyo, Inc., Basking Ridge, NJ 07920, USA
| | - Yoko Tanaka
- Daiichi Sankyo, Inc., Basking Ridge, NJ 07920, USA
| | - Yan Xiong
- Daiichi Sankyo, Inc., Basking Ridge, NJ 07920, USA
| | | | - Mats Rosenlund
- Daiichi Sankyo Europe, Munich, 81379, Germany
- Department of Learning, Informatics, Management & Ethics (LIME), Karolinska Institutet, Stockholm, 171 77, Sweden
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Zhu AY, Roy D, Zhu Z, Sailer MO. Propensity score stratified MAP prior and posterior inference for incorporating information across multiple potentially heterogeneous data sources. J Biopharm Stat 2024; 34:190-204. [PMID: 36882957 DOI: 10.1080/10543406.2023.2181354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 02/10/2023] [Indexed: 03/09/2023]
Abstract
Incorporation of external information is becoming increasingly common when designing clinical trials. Availability of multiple sources of information has inspired the development of methodologies that account for potential heterogeneity not only between the prospective trial and the pooled external data sources but also between the different external data sources themselves. Our approach proposes an intuitive way of handling such a scenario for the continuous outcomes setting by using propensity score-based stratification and then utilizing robust meta-analytic predictive priors for each stratum to incorporate the prior data to distinguish among different external data sources in each stratum. Through extensive simulations, our approach proves to be more efficient and less biased than the currently available methods. A real case study using clinical trials that study schizophrenia from multiple different sources is also included.
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Affiliation(s)
- Angela Yaqian Zhu
- Statistics and Decision Sciences, Janssen Research & Development, Johnson & Johnson, Raritan, New Jersey, USA
| | - Dooti Roy
- Department of Biostatistics and Data Science, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Zheng Zhu
- Department of Biostatistics and Data Science, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Martin Oliver Sailer
- Department of Biostatistics and Data Science, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
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Cordero M, Meinfelder F, Eilert T. A Modern Approach to Stability Studies via Bayesian Linear Mixed Models Incorporating Auxiliary Effects. J Pharm Sci 2024:S0022-3549(24)00061-3. [PMID: 38417792 DOI: 10.1016/j.xphs.2024.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 03/01/2024]
Abstract
In preparation to the launch of a pharmaceutical product, an estimate of its shelf life via stability testing is required by regulatory agencies. The ICH-Q1E guidance has been the worldwide reference to reach this objective, but in recent years several authors have criticized many of its aspects. To that end we discuss a complete Bayesian transcript of the ICH-Q1E, treating all the apparent shortcomings, while also addressing the presence of multiple batches using a linear mixed model (LMM) for proper shelf life prediction by explicitly modelling the batch-to-batch variability. This comprises a redefinition of the linear models proposed in the ICH-Q1E by suitable LMM counterparts, and a Bayesian analogue for model selection, which is more intuitive and remedies detrimental features of the ICH approach. In that context, a proper mathematical foundation of shelf life is provided that we use to investigate and mathematically compare the two available approaches to shelf life determination via shelf life distribution and batch distribution. The discussed method is then tested and evaluated using real data in comparison with the ICH-Q1E approach demonstrating their approximate equivalency for 6 batches. As a major objective, we extended the LMM with auxiliary fixed effects, here the concentration, which interconnect data sets allowing a prediction of shelf lives for concentrations lacking a sufficient number of batches. This establishes a novel approach to accelerate the speed to submission while retaining the patients' safety. Both case studies underline the inherent superiority of LMMs within a Bayesian framework regarding predictability and interpretability, and we hope that the relevant authorities will accept this approach in the future.
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Affiliation(s)
- Miguel Cordero
- Chair of Statistics and Econometrics, University of Bamberg, Feldkirchenstraße 21, D-96052 Bamberg
| | - Florian Meinfelder
- Chair of Statistics and Econometrics, University of Bamberg, Feldkirchenstraße 21, D-96052 Bamberg
| | - Tobias Eilert
- Boehringer Ingelheim Pharma GmbH & Co. KG, CMC Statistics BioPharma, Birkendorfer Straße 65, D-88397 Biberach an der Riß, Germany
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Saville BR, Burkhoff D, Abraham WT. Streamlining Randomized Clinical Trials for Device Therapies in Heart Failure: Bayesian Borrowing of External Data. J Am Heart Assoc 2024; 13:e033255. [PMID: 38258663 DOI: 10.1161/jaha.123.033255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024]
Abstract
BACKGROUND The Breakthrough Devices Program of the US Food and Drug Administration has accelerated the development and evaluation of medical devices for patients with heart failure. One such device is the Optimizer Smart System, which the US Food and Drug Administration approved in 2019. METHODS AND RESULTS The Optimizer device was evaluated in a pivotal randomized clinical trial (FIX-HF-5C [Confirmatory Randomized Trial Evaluating the Optimizer System]) that leveraged Bayesian borrowing of external data to reduce the sample size and determine therapeutic device benefit versus continued medical therapy. Bayesian borrowing is explained in the context of the FIX-HF-5C trial, including an overview of the statistical methodologies, regulatory considerations, and interpretations of trial results. CONCLUSIONS The US Food and Drug Administration Breakthrough Devices Program and novel Bayesian statistical methodology accelerated the path to regulatory approval and patient access to a potentially lifesaving device and may serve as a model for future clinical trials.
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Affiliation(s)
- Benjamin R Saville
- Berry Consultants, LLC Austin TX USA
- Department of Biostatistics Vanderbilt University Medical Center Nashville TN USA
| | | | - William T Abraham
- Division of Cardiovascular Medicine The Ohio State University Columbus OH USA
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Bi D, Liu M, Lin J, Liu R. BEATS: Bayesian hybrid design with flexible sample size adaptation for time-to-event endpoints. Stat Med 2023; 42:5708-5722. [PMID: 37858287 DOI: 10.1002/sim.9936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 07/17/2023] [Accepted: 10/05/2023] [Indexed: 10/21/2023]
Abstract
As the roles of historical trials and real-world evidence in drug development have substantially increased, several approaches have been proposed to leverage external data and improve the design of clinical trials. While most of these approaches focus on methodology development for borrowing information during the analysis stage, there is a risk of inadequate or absent enrollment of concurrent control due to misspecification of heterogeneity from external data, which can result in unreliable estimates of treatment effect. In this study, we introduce a Bayesian hybrid design with flexible sample size adaptation (BEATS) that allows for adaptive borrowing of external data based on the level of heterogeneity to augment the control arm during both the design and interim analysis stages. Moreover, BEATS extends the Bayesian semiparametric meta-analytic predictive prior (BaSe-MAP) to incorporate time-to-event endpoints, enabling optimal borrowing performance. Initially, BEATS calibrates the expected sample size and initial randomization ratio based on heterogeneity among the external data. During the interim analysis, flexible sample size adaptation is performed to address conflicts between the concurrent and historical control, while also conducting futility analysis. At the final analysis, estimation is provided by incorporating the calibrated amount of external data. Therefore, our proposed design allows for an approximation of an ideal randomized controlled trial with an equal randomization ratio while controlling the size of the concurrent control to benefit patients and accelerate drug development. BEATS also offers optimal power and robust estimation through flexible sample size adaptation when conflicts arise between the concurrent control and external data.
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Affiliation(s)
- Dehua Bi
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Meizi Liu
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Jianchang Lin
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Rachael Liu
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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Majumdar A, Rothwell R, Reaman G, Ahlberg C, Roy P. Utility of propensity score-based Bayesian borrowing of external adult data in pediatric trials: A pragmatic evaluation through a case study in acute lymphoblastic leukemia (ALL). J Biopharm Stat 2023; 33:737-751. [PMID: 36600441 DOI: 10.1080/10543406.2022.2162069] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023]
Abstract
A fully powered randomized controlled cancer trial can be challenging to conduct in children because of difficulties in enrollment of pediatric patients due to low disease incidence. One way to improve the feasibility of trials in pediatric patients, when clinically appropriate, is through borrowing information from comparable external adult trials in the same disease. Bayesian analysis of a pediatric trial provides a way of seamlessly augmenting pediatric trial efficacy data with data from external adult trials. However, not all external adult trial subjects may be equally clinically relevant with respect to the baseline disease severity, prognostic factors, co-morbidities, and prior therapy observed in the pediatric trial of interest. The propensity score matching method provides a way of matching the external adult subjects to the pediatric trial subjects on a set of clinically determined baseline covariates, such as baseline disease severity, prognostic factors and prior therapy. The matching then allows Bayesian information borrowing from only the most clinically relevant external adult subjects. Through a case study in pediatric acute lymphoblastic leukemia (ALL), we examine the utility of propensity score matched mixture and power priors in bringing appropriate external adult efficacy information into pediatric trial efficacy assessment, and present considerations for scaling fixed borrowing from external adult data.
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Affiliation(s)
- Antara Majumdar
- Oncology Biostatistics, GlaxoSmithKline, Collegeville, PA, USA
| | - Rebecca Rothwell
- Office of Biostatistics, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Gregory Reaman
- Oncology Center of Excellence, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Corinne Ahlberg
- Acorn AI by Medidata, a Dassault Systèmes company, New York, NY, USA
| | - Pourab Roy
- Biostatistics, Regeneron Pharmaceuticals, Tarrytown, NY, USA
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Abstract
In many therapeutic areas with unmet medical needs, such as pediatric oncology and rare diseases, one of the deterrent factors for clinical trial interpretability is the limited sample size with less-than-ideal operating characteristics. Single arm is usually the only viable design due to feasibility and ethical concerns. For the trial results to be more interpretable and conclusive, the evaluation of operating characteristics, such as type I error rate and power, and the appropriate utilization of prior information for study design, shall be prespecified and fully investigated during the trial planning phase. So far, very few existing literature addressed optimal sample size determination issues for the planning of pediatric and rare population trials, with majority of research focusing on analysis perspective with focus on Bayesian borrowing. In practice, when a single-arm trial is designed for rare population, it is not uncommon that the only information available is from an earlier trial and/or a few clinical publications based on observational studies, often constituting mixed or uncertain conclusions. In light of this, an optimal Bayesian sample size determination method for single-arm trial with binary or continuous endpoint is proposed, where conflicting prior beliefs can be readily incorporated. Prior effective sample size can be calculated to assess the robustness as well as the prior information borrowed. Moreover, due to the lack of closed-form posterior distributions in general, an alternative approach for calculating Bayesian power is described. Simulation studies are provided to demonstrate the utility of the proposed methods. In addition, a case study in pediatric patients with leukemia is included to illustrate the proposed method with the existing approaches.
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Affiliation(s)
- Ziyu Ji
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States
| | - Junjing Lin
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, United States
| | - Jianchang Lin
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, United States
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Liu M, Bunn V, Hupf B, Lin J, Lin J. Propensity-score-based meta-analytic predictive prior for incorporating real-world and historical data. Stat Med 2021; 40:4794-4808. [PMID: 34126656 DOI: 10.1002/sim.9095] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 05/07/2021] [Accepted: 05/27/2021] [Indexed: 01/20/2023]
Abstract
As the availability of real-world data sources (eg, EHRs, claims data, registries) and historical data has rapidly surged in recent years, there is an increasing interest and need from investigators and health authorities to leverage all available information to reduce patient burden and accelerate both drug development and regulatory decision making. Bayesian meta-analytic approaches are a popular historical borrowing method that has been developed to leverage such data using robust hierarchical models. The model structure accounts for various degrees of between-trial heterogeneity, resulting in adaptively discounting the external information in the case of data conflict. In this article, we propose to integrate the propensity score method and Bayesian meta-analytic-predictive (MAP) prior to leverage external real-world and historical data. The propensity score methodology is applied to select a subset of patients from external data that are similar to those in the current study with regards to key baseline covariates and to stratify the selected patients together with those in the current study into more homogeneous strata. The MAP prior approach is used to obtain stratum-specific MAP prior and derive the overall propensity score integrated meta-analytic predictive (PS-MAP) prior. Additionally, we allow for tuning the prior effective sample size for the proposed PS-MAP prior, which quantifies the amount of information borrowed from external data. We evaluate the performance of the proposed PS-MAP prior by comparing it to the existing propensity score-integrated power prior approach in a simulation study and illustrate its implementation with an example of a single-arm phase II trial.
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Affiliation(s)
- Meizi Liu
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Veronica Bunn
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Bradley Hupf
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Junjing Lin
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Jianchang Lin
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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