1
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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 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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2
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Wei W, Blaha O, Esserman D, Zelterman D, Kane M, Liu R, Lin J. A Bayesian platform trial design with hybrid control based on multisource exchangeability modelling. Stat Med 2024; 43:2439-2451. [PMID: 38594809 DOI: 10.1002/sim.10077] [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: 11/09/2022] [Revised: 02/25/2024] [Accepted: 03/27/2024] [Indexed: 04/11/2024]
Abstract
Enrolling patients to the standard of care (SOC) arm in randomized clinical trials, especially for rare diseases, can be very challenging due to the lack of resources, restricted patient population availability, and ethical considerations. As the therapeutic effect for the SOC is often well documented in historical trials, we propose a Bayesian platform trial design with hybrid control based on the multisource exchangeability modelling (MEM) framework to harness historical control data. The MEM approach provides a computationally efficient method to formally evaluate the exchangeability of study outcomes between different data sources and allows us to make better informed data borrowing decisions based on the exchangeability between historical and concurrent data. We conduct extensive simulation studies to evaluate the proposed hybrid design. We demonstrate the proposed design leads to significant sample size reduction for the internal control arm and borrows more information compared to competing Bayesian approaches when historical and internal data are compatible.
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Affiliation(s)
- Wei Wei
- 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
| | - Denise Esserman
- 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
| | - Michael Kane
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Rachael Liu
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Jianchang Lin
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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3
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Polley MYC, Schwartz D, Karrison T, Dignam JJ. Leveraging external control data in the design and analysis of neuro-oncology trials: Pearls and perils. Neuro Oncol 2024; 26:796-810. [PMID: 38254183 PMCID: PMC11066907 DOI: 10.1093/neuonc/noae005] [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: 07/13/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Randomized controlled trials have been the gold standard for evaluating medical treatments for many decades but they are often criticized for requiring large sample sizes. Given the urgent need for better therapies for glioblastoma, it has been argued that data collected from patients treated with the standard regimen can provide high-quality external control data to supplement or replace concurrent control arm in future glioblastoma trials. METHODS In this article, we provide an in-depth appraisal of the use of external control data in the context of neuro-oncology trials. We describe several clinical trial designs with particular attention to how external information is utilized and address common fallacies that may lead to inappropriate adoptions of external control data. RESULTS Using 2 completed glioblastoma trials, we illustrate the use of an assessment tool that lays out a blueprint for assembling a high-quality external control data set. Using statistical simulations, we draw caution from scenarios where these approaches can fall short on controlling the type I error rate. CONCLUSIONS While this approach may hold promise in generating informative data in certain settings, this sense of optimism should be tampered with a healthy dose of skepticism due to a myriad of design and analysis challenges articulated in this review. Importantly, careful planning is key to its successful implementation.
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Affiliation(s)
- Mei-Yin C Polley
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
- NRG Oncology Statistics and Data Management Center, Philadelphia, Pennsylvania, USA
| | - Daniel Schwartz
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Theodore Karrison
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
- NRG Oncology Statistics and Data Management Center, Philadelphia, Pennsylvania, USA
| | - James J Dignam
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
- NRG Oncology Statistics and Data Management Center, Philadelphia, Pennsylvania, USA
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4
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Viraswami-Appanna K, Buenconsejo J, Baidoo C, Chan I, Li D, Micsinai-Balan M, Tiwari R, Yang L, Sethuraman V. Accelerating drug development at Bristol Myers Squibb through innovation. Drug Discov Today 2024; 29:103952. [PMID: 38508230 DOI: 10.1016/j.drudis.2024.103952] [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: 12/30/2023] [Revised: 03/07/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024]
Abstract
This paper focuses on the use of novel technologies and innovative trial designs to accelerate evidence generation and increase pharmaceutical Research and Development (R&D) productivity, at Bristol Myers Squibb. We summarize learnings with case examples, on how we prepared and continuously evolved to address the increasing cost, complexities, and external pressures in drug development, to bring innovative medicines to patients much faster. These learnings were based on review of internal efforts toward accelerating R&D focusing on four key areas: adopting innovative trial designs, optimizing trial designs, leveraging external control data, and implementing novel methods using artificial intelligence and machine learning.
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Affiliation(s)
| | - Joan Buenconsejo
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Charlotte Baidoo
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Ivan Chan
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Daniel Li
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | | | - Ram Tiwari
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Ling Yang
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Venkat Sethuraman
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
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5
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van der Maas NG, Versluis J, Nasserinejad K, van Rosmalen J, Pabst T, Maertens J, Breems D, Manz M, Cloos J, Ossenkoppele GJ, Floisand Y, Gradowska P, Löwenberg B, Huls G, Postmus D, Pignatti F, Cornelissen JJ. Bayesian interim analysis for prospective randomized studies: reanalysis of the acute myeloid leukemia HOVON 132 clinical trial. Blood Cancer J 2024; 14:56. [PMID: 38538587 PMCID: PMC10973506 DOI: 10.1038/s41408-024-01037-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/07/2024] Open
Abstract
Randomized controlled trials (RCTs) are the gold standard to establish the benefit-risk ratio of novel drugs. However, the evaluation of mature results often takes many years. We hypothesized that the addition of Bayesian inference methods at interim analysis time points might accelerate and enforce the knowledge that such trials may generate. In order to test that hypothesis, we retrospectively applied a Bayesian approach to the HOVON 132 trial, in which 800 newly diagnosed AML patients aged 18 to 65 years were randomly assigned to a "7 + 3" induction with or without lenalidomide. Five years after the first patient was recruited, the trial was negative for its primary endpoint with no difference in event-free survival (EFS) between experimental and control groups (hazard ratio [HR] 0.99, p = 0.96) in the final conventional analysis. We retrospectively simulated interim analyses after the inclusion of 150, 300, 450, and 600 patients using a Bayesian methodology to detect early lack of efficacy signals. The HR for EFS comparing the lenalidomide arm with the control treatment arm was 1.21 (95% CI 0.81-1.69), 1.05 (95% CI 0.86-1.30), 1.00 (95% CI 0.84-1.19), and 1.02 (95% CI 0.87-1.19) at interim analysis 1, 2, 3 and 4, respectively. Complete remission rates were lower in the lenalidomide arm, and early deaths more frequent. A Bayesian approach identified that the probability of a clinically relevant benefit for EFS (HR < 0.76, as assumed in the statistical analysis plan) was very low at the first interim analysis (1.2%, 0.6%, 0.4%, and 0.1%, respectively). Similar observations were made for low probabilities of any benefit regarding CR. Therefore, Bayesian analysis significantly adds to conventional methods applied for interim analysis and may thereby accelerate the performance and completion of phase III trials.
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Affiliation(s)
- Niek G van der Maas
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jurjen Versluis
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Kazem Nasserinejad
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Joost van Rosmalen
- Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
| | - Thomas Pabst
- University Hospital, Inselspital, Bern, Switzerland
- Swiss Group for Clinical Cancer Research (SAKK), Bern, Switzerland
| | | | | | - Markus Manz
- Swiss Group for Clinical Cancer Research (SAKK), Bern, Switzerland
- University Hospital Zurich, Zurich, Switzerland
| | - Jacqueline Cloos
- Amsterdam UMC, location VUMC, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Gert J Ossenkoppele
- Amsterdam UMC, location VUMC, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | | | - Patrycja Gradowska
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
- HOVON Foundation, Rotterdam, the Netherlands
| | - Bob Löwenberg
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Gerwin Huls
- University Medical Center, University Groningen, Groningen, the Netherlands
| | - Douwe Postmus
- Oncology and Hematology Office, European Medicines Agency, Amsterdam, the Netherlands
| | - Francesco Pignatti
- Oncology and Hematology Office, European Medicines Agency, Amsterdam, the Netherlands
| | - Jan J Cornelissen
- Department of Hematology, Erasmus Medical Center Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands.
- Oncology and Hematology Office, European Medicines Agency, Amsterdam, the Netherlands.
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6
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Tong L, Li C, Xia J, Wang L. A Bayesian approach based on discounting factor for consistency assessment in multi-regional clinical trial. J Biopharm Stat 2024:1-17. [PMID: 38506674 DOI: 10.1080/10543406.2024.2328591] [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: 10/27/2022] [Accepted: 03/05/2024] [Indexed: 03/21/2024]
Abstract
Multi-regional clinical trial (MRCT) has become an increasing trend for its supporting simultaneous global drug development. After MRCT, consistency assessment needs to be conducted to evaluate regional efficacy. The weighted Z-test approach is a common consistency assessment approach in which the weighting parameter W does not have a good practical significance; the discounting factor approach improved from the weighted Z-test approach by converting the estimation of W in original weighted Z-test approach to the estimation of discounting factor D. However, the discounting factor approach is an approach of frequency statistics, in which D was fixed as a certain value; the variation of D was not considered, which may lead to un-reasonable results. In this paper, we proposed a Bayesian approach based on D to evaluate the treatment effect for the target region in MRCT, in which the variation of D was considered. Specifically, we first took D random instead of fixed as a certain value and specified a beta distribution for it. According to the results of simulation, we further adjusted the Bayesian approach. The application of the proposed approach was illustrated by Markov Chain Monte Carlo simulation.
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Affiliation(s)
- Liang Tong
- Department of Health Statistics, Faculty of Preventive Medicine, Air Force Medical University, Xi'an, Shaanxi, China
- Center for Disease Control and Prevention of Central Theater Command, Beijing, China
| | - Chen Li
- Department of Health Statistics, Faculty of Preventive Medicine, Air Force Medical University, Xi'an, Shaanxi, China
- Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, Shaanxi, China
| | - Jielai Xia
- Department of Health Statistics, Faculty of Preventive Medicine, Air Force Medical University, Xi'an, Shaanxi, China
- Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, Shaanxi, China
| | - Ling Wang
- Department of Health Statistics, Faculty of Preventive Medicine, Air Force Medical University, Xi'an, Shaanxi, China
- Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, Shaanxi, China
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7
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Kopp-Schneider A, Wiesenfarth M, Held L, Calderazzo S. Simulating and reporting frequentist operating characteristics of clinical trials that borrow external information: Towards a fair comparison in case of one-arm and hybrid control two-arm trials. Pharm Stat 2024; 23:4-19. [PMID: 37632266 DOI: 10.1002/pst.2334] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 07/25/2023] [Accepted: 08/01/2023] [Indexed: 08/27/2023]
Abstract
Borrowing information from historical or external data to inform inference in a current trial is an expanding field in the era of precision medicine, where trials are often performed in small patient cohorts for practical or ethical reasons. Even though methods proposed for borrowing from external data are mainly based on Bayesian approaches that incorporate external information into the prior for the current analysis, frequentist operating characteristics of the analysis strategy are often of interest. In particular, type I error rate and power at a prespecified point alternative are the focus. We propose a procedure to investigate and report the frequentist operating characteristics in this context. The approach evaluates type I error rate of the test with borrowing from external data and calibrates the test without borrowing to this type I error rate. On this basis, a fair comparison of power between the test with and without borrowing is achieved. We show that no power gains are possible in one-sided one-arm and two-arm hybrid control trials with normal endpoint, a finding proven in general before. We prove that in one-arm fixed-borrowing situations, unconditional power (i.e., when external data is random) is reduced. The Empirical Bayes power prior approach that dynamically borrows information according to the similarity of current and external data avoids the exorbitant type I error inflation occurring with fixed borrowing. In the hybrid control two-arm trial we observe power reductions as compared to the test calibrated to borrowing that increase when considering unconditional power.
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Affiliation(s)
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Leonhard Held
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Silvia Calderazzo
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
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8
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Calderazzo S, Wiesenfarth M, Kopp-Schneider A. Robust incorporation of historical information with known type I error rate inflation. Biom J 2024; 66:e2200322. [PMID: 38063813 DOI: 10.1002/bimj.202200322] [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: 11/29/2022] [Revised: 07/30/2023] [Accepted: 09/23/2023] [Indexed: 01/30/2024]
Abstract
Bayesian clinical trials can benefit from available historical information through the specification of informative prior distributions. Concerns are however often raised about the potential for prior-data conflict and the impact of Bayes test decisions on frequentist operating characteristics, with particular attention being assigned to inflation of type I error (TIE) rates. This motivates the development of principled borrowing mechanisms, that strike a balance between frequentist and Bayesian decisions. Ideally, the trust assigned to historical information defines the degree of robustness to prior-data conflict one is willing to sacrifice. However, such relationship is often not directly available when explicitly considering inflation of TIE rates. We build on available literature relating frequentist and Bayesian test decisions, and investigate a rationale for inflation of TIE rate which explicitly and linearly relates the amount of borrowing and the amount of TIE rate inflation in one-arm studies. A novel dynamic borrowing mechanism tailored to hypothesis testing is additionally proposed. We show that, while dynamic borrowing prevents the possibility to obtain a simple closed-form TIE rate computation, an explicit upper bound can still be enforced. Connections with the robust mixture prior approach, particularly in relation to the choice of the mixture weight and robust component, are made. Simulations are performed to show the properties of the approach for normal and binomial outcomes, and an exemplary application is demonstrated in a case study.
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Affiliation(s)
- Silvia Calderazzo
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Manuel Wiesenfarth
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
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9
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Yang J, Li G. Enhancing generalizability and efficiency in clinical trials through dynamic information borrowing for both experimental and control arms: A simulation study. J Evid Based Med 2023; 16:547-556. [PMID: 38148281 DOI: 10.1111/jebm.12574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 12/06/2023] [Indexed: 12/28/2023]
Abstract
AIM Utilizing external information in clinical trials enhances validity by including a wider population and expedites the implementation of adaptive designs, ultimately improving research efficiency. However, current research focused on scenarios in which only the control group benefited from the utilization of external information, while trials involving external information in both experimental and control arms were more complex and might pose challenges when applied in real-world settings. METHODS To address these concerns, our study pioneered the application of test-then-pool, normalized power prior, calibrated power prior, and elastic prior to a two-arm information borrowing framework and systematically compared their operating characteristics through a series of simulation studies under most and least desirable scenarios. RESULTS In the most desirable scenarios of information borrowing, all methods managed to control the mean of type I error rates within 5%, among which the normalized power prior, calibrated power prior and elastic prior approaches increased the mean of power from 85.94% to 95%. In the least desirable scenarios, the mean type I error rates for normalized power prior, calibrated power prior and elastic prior approaches exceeded 20%, while the mean power decreased to around 80%. CONCLUSIONS Our findings reveal that the normalized power prior, calibrated power prior and elastic prior approaches are suitable for situations with minimal heterogeneity between historical and current data, whereas the test-then-pool approach emerges as a more prudent choice when facing substantial discrepancies between historical and current information for trials consider information borrow in both arms.
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Affiliation(s)
- Jiaying Yang
- Department of Public Health, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Guochun Li
- Department of Public Health, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
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10
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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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11
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Travis J, Rothmann M, Thomson A. Perspectives on informative Bayesian methods in pediatrics. J Biopharm Stat 2023; 33:830-843. [PMID: 36710384 DOI: 10.1080/10543406.2023.2170405] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/15/2023] [Indexed: 01/31/2023]
Abstract
Bayesian methods have been proposed as a natural fit for pediatric extrapolation, as they allow the incorporation of relevant external data to reduce the required sample size and hence trial burden for the pediatric patient population. In this paper we will discuss our experience and perspectives with these methods in pediatric trials. We will present some of the background and thinking underlying pediatric extrapolation and discuss the use of Bayesian methods within this context. We will present two recent case examples illustrating the value of a Bayesian approach in this setting and present perspectives on some of the issues that we have encountered in these and other cases.
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Affiliation(s)
- James Travis
- Office of Biostatistics, Office of Translational Science, Center for the Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Mark Rothmann
- Office of Biostatistics, Office of Translational Science, Center for the Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Andrew Thomson
- Data Analytics and Methods Taskforce, European Medicines Agency, Amsterdam, NL
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12
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Spanakis E, Kron M, Bereswill M, Mukhopadhyay S. Addressing statistical issues when leveraging external control data in pediatric clinical trials using Bayesian dynamic borrowing. J Biopharm Stat 2023; 33:752-769. [PMID: 36507718 DOI: 10.1080/10543406.2022.2152833] [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: 03/24/2022] [Accepted: 11/23/2022] [Indexed: 12/15/2022]
Abstract
Conducting a well-powered and adequately controlled clinical trial in children is often challenging. Bayesian approaches are an attractive option for addressing such challenges as they provide a quantitatively rigorous and integrated framework that makes use of current control data to check and borrow information from historical control data. However various practical concerns and related statistical issues emerge when implementing such Bayesian borrowing approaches. In this manuscript we use a motivating case study to discuss a rigorous stepwise approach on how to address those issues within the Bayesian framework. Specifically, a comprehensive quantitative framework is proposed to assess the extent, synergy, and impact of borrowing. Steps on computing the measures to interpret borrowing are illustrated. Those measures can further help to determine whether additional discounting of external information is necessary.
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Affiliation(s)
- Emmanouil Spanakis
- AbbVie Deutschland GmbH & Co KG, Data and Statistical Sciences, Ludwigshafen, Germany
| | - Martina Kron
- AbbVie Deutschland GmbH & Co KG, Data and Statistical Sciences, Ludwigshafen, Germany
| | - Mareike Bereswill
- AbbVie Deutschland GmbH & Co KG, Data and Statistical Sciences, Ludwigshafen, Germany
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13
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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] [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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14
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Cooner F, Ye J, Reaman G. Clinical trial considerations for pediatric cancer drug development. J Biopharm Stat 2023; 33:859-874. [PMID: 36749066 DOI: 10.1080/10543406.2023.2172424] [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: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 02/08/2023]
Abstract
Oncology has been one of the most active therapeutic areas in medicinal products development. Despite this fact, few drugs have been approved for use in pediatric cancer patients when compared to the number approved for adults with cancer. This disparity could be attributed to the fact that many oncology drugs have had orphan drug designation and were exempt from Pediatric Research Equity Act (PREA) requirements. On August 18, 2017, the RACE for Children Act, i.e. Research to Accelerate Cures and Equity Act, was signed into law as Title V of the 2017 FDA Reauthorization Act (FDARA) to amend the PREA. Pediatric investigation is now required if the drug or biological product is intended for the treatment of an adult cancer and directed at a molecular target that FDA determines to be "substantially relevant to the growth or progression of a pediatric cancer." This paper discusses the specific considerations in clinical trial designs and statistical methodologies to be implemented in oncology pediatric clinical programs.
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Affiliation(s)
- Freda Cooner
- Global Biostatistics, Amgen Inc, Thousand Oaks, CA, USA
| | - Jingjing Ye
- Global Statistics and Data Sciences (GSDS), BeiGene USA, Fulton, MD, USA
| | - Gregory Reaman
- Oncology Center of Excellence, Office of the Commissioner, U.S. FDA, Silver Spring, MD, USA
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15
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Li R, Lin R, Huang J, Tian L, Zhu J. A frequentist approach to dynamic borrowing. Biom J 2023; 65:e2100406. [PMID: 37189217 DOI: 10.1002/bimj.202100406] [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: 12/22/2021] [Revised: 11/04/2022] [Accepted: 02/17/2023] [Indexed: 05/17/2023]
Abstract
There has been growing interest in leveraging external control data to augment a randomized control group data in clinical trials and enable more informative decision making. In recent years, the quality and availability of real-world data have improved steadily as external controls. However, information borrowing by directly pooling such external controls with randomized controls may lead to biased estimates of the treatment effect. Dynamic borrowing methods under the Bayesian framework have been proposed to better control the false positive error. However, the numerical computation and, especially, parameter tuning, of those Bayesian dynamic borrowing methods remain a challenge in practice. In this paper, we present a frequentist interpretation of a Bayesian commensurate prior borrowing approach and describe intrinsic challenges associated with this method from the perspective of optimization. Motivated by this observation, we propose a new dynamic borrowing approach using adaptive lasso. The treatment effect estimate derived from this method follows a known asymptotic distribution, which can be used to construct confidence intervals and conduct hypothesis tests. The finite sample performance of the method is evaluated through extensive Monte Carlo simulations under different settings. We observed highly competitive performance of adaptive lasso compared to Bayesian approaches. Methods for selecting tuning parameters are also thoroughly discussed based on results from numerical studies and an illustration example.
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Affiliation(s)
- Ruilin Li
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, USA
| | - Ray Lin
- Genentech, Inc., PD Data Sciences, San Francisco, California, USA
| | - Jiangeng Huang
- Genentech, Inc., PD Data Sciences, San Francisco, California, USA
| | - Lu Tian
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA
| | - Jiawen Zhu
- Genentech, Inc., PD Data Sciences, San Francisco, California, USA
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16
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Han L, Deng Q, He Z, Fleischer F, Yu F. Bayesian hierarchical model for dose-finding trial incorporating historical data. J Biopharm Stat 2023:1-15. [PMID: 37676029 DOI: 10.1080/10543406.2023.2251578] [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: 04/29/2022] [Accepted: 08/17/2023] [Indexed: 09/08/2023]
Abstract
The Multiple Comparison Procedure and Modelling (MCPMod) approach has been shown to be a powerful statistical technique that can significantly improve the design and analysis of dose-finding studies under model uncertainty. Due to its frequentist nature, however, it is difficult to incorporate information into MCPMod from historical trials on the same drug. BMCPMod, a recently introduced Bayesian version of MCPMod, is designed to take into account historical information on the placebo dose group. We introduce a Bayesian hierarchical framework capable of incorporating historical information on an arbitrary number of dose groups, including both placebo and active ones, taking into account the relationship between responses of these dose groups. Our approach can also model both prognostic and predictive between-trial heterogeneity and is particularly useful in situations where the effect sizes of two trials are different. Our goal is to reduce the necessary sample size in the dose-finding trial while maintaining its target power.
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Affiliation(s)
- Linxi Han
- School of Mathematics, University of Bristol, Bristol, UK
| | - Qiqi Deng
- Biostatistics and Data Sciences, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Zhangyi He
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Frank Fleischer
- Biostatistics+Data Sciences Corp, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Feng Yu
- School of Mathematics, University of Bristol, Bristol, UK
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17
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Kaizer AM, Belli HM, Ma Z, Nicklawsky AG, Roberts SC, Wild J, Wogu AF, Xiao M, Sabo RT. Recent innovations in adaptive trial designs: A review of design opportunities in translational research. J Clin Transl Sci 2023; 7:e125. [PMID: 37313381 PMCID: PMC10260347 DOI: 10.1017/cts.2023.537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/29/2023] [Accepted: 04/17/2023] [Indexed: 06/15/2023] Open
Abstract
Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.
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Affiliation(s)
- Alexander M. Kaizer
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Hayley M. Belli
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Zhongyang Ma
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Andrew G. Nicklawsky
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Samantha C. Roberts
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jessica Wild
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Adane F. Wogu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Mengli Xiao
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Roy T. Sabo
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
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18
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Akalu Banbeta, Lesaffre E, Martina R, van Rosmalen J. Bayesian Borrowing Methods for Count Data: Analysis of Incontinence Episodes in Patients with Overactive Bladder. Stat Biopharm Res 2023. [DOI: 10.1080/19466315.2023.2190933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Affiliation(s)
- Akalu Banbeta
- I-Biostat, UHasselt, Hasselt, Belgium
- Department of Statistics, Jimma University, Jimma, Ethiopia
| | | | - Reynaldo Martina
- Faculty of Science, Technology, Engineering and Mathematics, Open University, Milton Keynes, UK
| | - Joost van Rosmalen
- Department of Biostatistics, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
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19
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Bayesian Statistics for Medical Devices: Progress Since 2010. Ther Innov Regul Sci 2023; 57:453-463. [PMID: 36869194 PMCID: PMC9984131 DOI: 10.1007/s43441-022-00495-w] [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: 09/08/2022] [Accepted: 12/24/2022] [Indexed: 03/05/2023]
Abstract
The use of Bayesian statistics to support regulatory evaluation of medical devices began in the late 1990s. We review the literature, focusing on recent developments of Bayesian methods, including hierarchical modeling of studies and subgroups, borrowing strength from prior data, effective sample size, Bayesian adaptive designs, pediatric extrapolation, benefit-risk decision analysis, use of real-world evidence, and diagnostic device evaluation. We illustrate how these developments were utilized in recent medical device evaluations. In Supplementary Material, we provide a list of medical devices for which Bayesian statistics were used to support approval by the US Food and Drug Administration (FDA), including those since 2010, the year the FDA published their guidance on Bayesian statistics for medical devices. We conclude with a discussion of current and future challenges and opportunities for Bayesian statistics, including artificial intelligence/machine learning (AI/ML) Bayesian modeling, uncertainty quantification, Bayesian approaches using propensity scores, and computational challenges for high dimensional data and models.
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20
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Kaplan D, Chen J, Yavuz S, Lyu W. Bayesian Dynamic Borrowing of Historical Information with Applications to the Analysis of Large-Scale Assessments. PSYCHOMETRIKA 2023; 88:1-30. [PMID: 35687222 PMCID: PMC9185721 DOI: 10.1007/s11336-022-09869-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 02/18/2022] [Indexed: 06/15/2023]
Abstract
The purpose of this paper is to demonstrate and evaluate the use of Bayesian dynamic borrowing (Viele et al, in Pharm Stat 13:41-54, 2014) as a means of systematically utilizing historical information with specific applications to large-scale educational assessments. Dynamic borrowing via Bayesian hierarchical models is a special case of a general framework of historical borrowing where the degree of borrowing depends on the heterogeneity among historical data and current data. A joint prior distribution over the historical and current data sets is specified with the degree of heterogeneity across the data sets controlled by the variance of the joint distribution. We apply Bayesian dynamic borrowing to both single-level and multilevel models and compare this approach to other historical borrowing methods such as complete pooling, Bayesian synthesis, and power priors. Two case studies using data from the Program for International Student Assessment reveal the utility of Bayesian dynamic borrowing in terms of predictive accuracy. This is followed by two simulation studies that reveal the utility of Bayesian dynamic borrowing over simple pooling and power priors in cases where the historical data is heterogeneous compared to the current data based on bias, mean squared error, and predictive accuracy. In cases of homogeneous historical data, Bayesian dynamic borrowing performs similarly to data pooling, Bayesian synthesis, and power priors. In contrast, for heterogeneous historical data, Bayesian dynamic borrowing performed at least as well, if not better, than other methods of borrowing with respect to mean squared error, percent bias, and leave-one-out cross-validation.
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Affiliation(s)
- David Kaplan
- University of Wisconsin - Madison, Madison, USA.
| | | | - Sinan Yavuz
- University of Wisconsin - Madison, Madison, USA
| | - Weicong Lyu
- University of Wisconsin - Madison, Madison, USA
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21
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Hickey GL, Parvu V, Zhang Y, Cooper CK, Wan Y. A pragmatic approach for dynamically incorporating predicate device data in prospective diagnostic test studies. J Biopharm Stat 2023; 33:77-89. [PMID: 35649152 DOI: 10.1080/10543406.2022.2080690] [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: 01/06/2023]
Abstract
Clinical studies are generally required to characterize the accuracy of new diagnostic tests. In some cases, historical data are available from a predicate device, which is directly relevant to the new test. If this data can be appropriately incorporated into the new test study design, there is an opportunity to reduce the sample size and trial duration for the new test. One approach to achieve this is the Bayesian power prior method, which allows for the historical information to be down-weighted via a power parameter. We propose a dynamic method to calculate the power parameter based on first comparing the data between the historical and new data sources using a one-sided comparison, and second mapping the comparison probability through a scaled-Weibull discount function to tune the effective sample size borrowed. This pragmatic and conservative approach is embedded in an adaptive trial framework allowing for the trial to stop early for success. An example is presented for a new test developed to detect Methicillin-resistant Staphylococcus aureus present in the nasal carriage.
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Affiliation(s)
- Graeme L Hickey
- Becton, Dickinson and Company, Franklin Lakes, New Jersey, USA
| | - Valentin Parvu
- Becton, Dickinson and Company, Franklin Lakes, New Jersey, USA
| | | | - Charles K Cooper
- Siemens Healthcare Diagnostics, Siemens Healthineers, Tarrytown, New York, USA
| | - Ying Wan
- Becton, Dickinson and Company, Franklin Lakes, New Jersey, USA
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22
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Li H, Jin M, Chung YC, Zhong S, Wang L. Bayesian Method of Borrowing Study-Level Historical Longitudinal Control Data for Mixed-Effects Models with Repeated Measures. Ther Innov Regul Sci 2023; 57:142-151. [PMID: 36315398 DOI: 10.1007/s43441-022-00449-2] [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: 03/01/2022] [Accepted: 08/12/2022] [Indexed: 12/23/2022]
Abstract
Bringing historical control information into a new trial appropriately holds the promise of more efficient trial design with more accurate estimates, increased power, and fewer patients allocated to inefficacious control group, provided the historical control data are sufficiently similar to the concurrent control. Interest has been growing over the past few decades in leveraging historical clinical trial on the control arm. However, most of the current historical borrowing methods focus on incorporating patient-level historical control information at only one time point. In this work, we propose a Bayesian hierarchical Mixed effect Models for Repeated Measures to incorporate aggregated study-level longitudinal historical control estimates into the concurrent trial that collected repeated longitudinal data. The simulation study demonstrates that, as compared to one time point data analysis approach, leveraging longitudinal historical control data produces greater power enhancement and mitigates the power loss when the missing data under missing at random mechanism is present. Our work also helps fill the gap of lack of methods borrowing historical longitudinal control data from the published summarized estimates when patient-level control data are not available.
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Affiliation(s)
- Hong Li
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals U.S.A., Inc., Lexington, USA.
| | - Man Jin
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals U.S.A., Inc., Lexington, USA
| | - Yu-Che Chung
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals U.S.A., Inc., Lexington, USA
| | - Sheng Zhong
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals U.S.A., Inc., Lexington, USA
| | - Li Wang
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals U.S.A., Inc., Lexington, USA
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23
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Garczarek U, Muehlemann N, Richard F, Yajnik P, Russek-Cohen E. Bayesian Strategies in Rare Diseases. Ther Innov Regul Sci 2022; 57:445-452. [PMID: 36566312 PMCID: PMC9789883 DOI: 10.1007/s43441-022-00485-y] [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: 09/28/2022] [Accepted: 11/22/2022] [Indexed: 12/25/2022]
Abstract
Bayesian strategies for planning and analyzing clinical trials have become a viable choice, especially in rare diseases where drug development faces many challenges and stakeholders are interested in innovations that may help overcome them. Disease natural history and clinical outcomes occurrence and variability are often poorly understood. Standard trial designs are not optimized to obtain adequate safety and efficacy data from small numbers of patients. Bayesian methods are well-suited for adaptive trials, with an accelerated learning curve. Using Bayesian statistics can be advantageous in that design choices and their consequences are considered carefully, continuously monitored, and updated where necessary, which ultimately provides a natural and principled way of seamlessly combining prior clinical information with data, within a solid decision theoretical framework. In this article, we introduce the Bayesian option in the rare disease context to support clinical decision-makers in selecting the best choice for their drug development project. Many researchers in drug development show reluctance to using Bayesian statistics, and the top-two reported barriers are insufficient knowledge of Bayesian approaches and a lack of clarity or guidance from regulators. Here we introduce concepts of borrowing, extrapolation, adaptation, and modeling and illustrate them with examples that have been discussed or developed with regulatory bodies to show how Bayesian strategies can be applied to drug development in rare diseases.
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24
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Chevret S, Timsit JF, Biard L. Challenges of using external data in clinical trials- an illustration in patients with COVID-19. BMC Med Res Methodol 2022; 22:321. [PMID: 36522698 PMCID: PMC9753019 DOI: 10.1186/s12874-022-01769-5] [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: 12/14/2021] [Accepted: 10/25/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND To improve the efficiency of clinical trials, leveraging external data on control and/or treatment effects, which is almost always available, appears to be a promising approach. METHODS We used data from the experimental arm of the Covidicus trial evaluating high-dose dexamethasone in severely ill and mechanically ventilated COVID-19 patients, using published data from the Recovery trial as external data, to estimate the 28-day mortality rate. Primary approaches to deal with external data were applied. RESULTS Estimates ranged from 0.241 ignoring the external data up to 0.294 using hierarchical Bayesian models. Some evidence of differences in mortality rates between the Covidicus and Recovery trials were observed, with an matched adjusted odds ratio of death in the Covidicus arm of 0.41 compared to the Recovery arm. CONCLUSIONS These indirect comparisons appear sensitive to the method used. None of those approaches appear robust enough to overcome randomized clinical trial data. TRIAL REGISTRATION Covidicus Trial: NCT04344730, First Posted: 14/04/2020; Recovery trial: NCT04381936.
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Affiliation(s)
- Sylvie Chevret
- Department of Biostatistics, Hôpital Saint-Louis, Paris, France
- ECSTRRA Team, INSERM U1153,Université de Paris, 75010 Paris, France
| | - Jean-François Timsit
- Medical and infectious diseases ICU, Hôpital Bichat-Claude-Bernard, 75018 Paris, France
| | - Lucie Biard
- Department of Biostatistics, Hôpital Saint-Louis, Paris, France
- ECSTRRA Team, INSERM U1153,Université de Paris, 75010 Paris, France
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25
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Overbey JR, Cheung YK, Bagiella E. Integrating non-concurrent controls in the analyses of late-entry experimental arms in multi-arm trials with a shared control group in the presence of parameter drift. Contemp Clin Trials 2022; 123:106972. [PMID: 36307007 DOI: 10.1016/j.cct.2022.106972] [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: 02/11/2022] [Revised: 10/14/2022] [Accepted: 10/20/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND Under a master protocol, open platform trials allow new experimental treatments to enter an existing clinical trial. Whether late-entry experimental treatments should be compared to all available or concurrently randomized controls is not well established. Using all available data can increase power and precision; however, drift in population parameters can yield biased estimates and impact type I error rate. METHODS We explored the application of methods developed to incorporate historical controls in two-arm trials to the analysis of a late-entry arm in a simulated open platform trial under varying scenarios of parameter drift. Methods explored include test-then-pool, fixed power prior, dynamic power prior, and multi-source exchangeability model approaches. RESULTS/CONCLUSIONS Simulated trial results confirm that in the presence of no drift, naively pooling all controls increases power and produces more precise, unbiased estimates when compared to using concurrent controls only. However, under drift, pooling can result in type I error rate inflation or deflation and biased estimates. In the presence of parameter drift, methods that partially borrow non-concurrent data, either through a static weighting mechanism or through methods that allow the heterogeneity between non-concurrent and concurrent data to determine the degree of borrowing, are superior to naively pooling the data. However, compared to using concurrent controls only, these approaches cannot guarantee type I error control or unbiased estimates. Thus, concurrent controls should be used as comparators in confirmatory studies.
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Affiliation(s)
- Jessica R Overbey
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Ying Kuen Cheung
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Emilia Bagiella
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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26
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Fang Y, Zhong S. The targeted virtual control approach for single-arm clinical trials with external controls. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2154260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Yixin Fang
- Department of Data and Statistical Sciences, AbbVie Inc
| | - Sheng Zhong
- Department of Data and Statistical Sciences, AbbVie Inc
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27
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Lin R, Shi H, Yin G, Thall PF, Yuan Y, Flowers CR. BAYESIAN HIERARCHICAL RANDOM-EFFECTS META-ANALYSIS AND DESIGN OF PHASE I CLINICAL TRIALS. Ann Appl Stat 2022; 16:2481-2504. [PMID: 36329718 PMCID: PMC9624503 DOI: 10.1214/22-aoas1600] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2024]
Abstract
We propose a curve-free random-effects meta-analysis approach to combining data from multiple phase I clinical trials to identify an optimal dose. Our method accounts for between-study heterogeneity that may stem from different study designs, patient populations, or tumor types. We also develop a meta-analytic-predictive (MAP) method based on a power prior that incorporates data from multiple historical studies into the design and conduct of a new phase I trial. Performances of the proposed methods for data analysis and trial design are evaluated by extensive simulation studies. The proposed random-effects meta-analysis method provides more reliable dose selection than comparators that rely on parametric assumptions. The MAP-based dose-finding designs are generally more efficient than those that do not borrow information, especially when the current and historical studies are similar. The proposed methodologies are illustrated by a meta-analysis of five historical phase I studies of Sorafenib, and design of a new phase I trial.
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Affiliation(s)
- Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - Haolun Shi
- Department of Statistics and Actuarial Science, Simon Fraser University, British Columbia, Canada
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong
| | - Peter F Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - Christopher R Flowers
- Department of Lymphoma/Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
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28
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Van Lancker K, Tarima S, Bartlett J, Bauer M, Bharani-Dharan B, Bretz F, Flournoy N, Michiels H, Olarte Parra C, Rosenberger JL, Cro S. Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2094459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Kelly Van Lancker
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Sergey Tarima
- Division of Biostatistics, Medical College of Wisconsin, U.S.A.
| | | | - Madeline Bauer
- Division of Infectious Diseases, Keck School of Medicine, University of Southern California (ret), Los Angeles, U.S.A.
| | | | - Frank Bretz
- Novartis Pharma AG, Basel, Switzerland
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Nancy Flournoy
- Department of Statistics, University of Missouri (emerita), Columbia, U.S.A.
| | - Hege Michiels
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | | | - James L Rosenberger
- National Institute of Statistical Sciences, and Department of Statistics, Penn State University, University Park, PA 16802-2111 U.S.A.
| | - Suzie Cro
- Imperial Clinical Trials Unit, Imperial College London, U.K
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29
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Yuan W, Chen MH, Zhong J. Bayesian Design of Superiority Trials: Methods and Applications. Stat Biopharm Res 2022; 14:433-443. [PMID: 36968644 PMCID: PMC10035591 DOI: 10.1080/19466315.2022.2090429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
In this paper, we lay out the basic elements of Bayesian sample size determination (SSD) for the Bayesian design of a two-arm superiority clinical trial. We develop a flowchart of the Bayesian SSD that highlights the critical components of a Bayesian design and provides a practically useful roadmap for designing a Bayesian clinical trial in real world applications. We empirically examine the amount of borrowing, the choice of noninformative priors, and the impact of model misspecification on the Bayesian type I error and power. A formal and statistically rigorous formulation of conditional borrowing within the decision rule framework is developed. Moreover, by extending the partial borrowing power priors, a new borrowing-by-parts power prior for incorporating historical data is proposed. Computational algorithms are also developed to calculate the Bayesian type I error and power. Extensive simulation studies are carried out to explore the operating characteristics of the proposed Bayesian design of a superiority trial.
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Affiliation(s)
- Wenlin Yuan
- Department of Statistics, University of Connecticut at Storrs, CT 06269
| | - Ming-Hui Chen
- Department of Statistics, University of Connecticut at Storrs, CT 06269
| | - John Zhong
- REGENXBIO Inc., 9804 Medical Center Drive, Rockville, MD 20850
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30
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De Santis F, Gubbiotti S. Borrowing historical information for non-inferiority trials on Covid-19 vaccines. Int J Biostat 2022:ijb-2021-0120. [PMID: 35472295 DOI: 10.1515/ijb-2021-0120] [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] [Received: 11/17/2021] [Accepted: 03/28/2022] [Indexed: 11/15/2022]
Abstract
Non-inferiority vaccine trials compare new candidates to active controls that provide clinically significant protection against a disease. Bayesian statistics allows to exploit pre-experimental information available from previous studies to increase precision and reduce costs. Here, historical knowledge is incorporated into the analysis through a power prior that dynamically regulates the degree of information-borrowing. We examine non-inferiority tests based on credible intervals for the unknown effects-difference between two vaccines on the log odds ratio scale, with an application to new Covid-19 vaccines. We explore the frequentist properties of the method and we address the sample size determination problem.
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Affiliation(s)
- Fulvio De Santis
- Dipartimento di Scienze Statistiche, Sapienza University of Rome, Roma, Italy
| | - Stefania Gubbiotti
- Dipartimento di Scienze Statistiche, Sapienza University of Rome, Roma, Italy
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31
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An Adaptive Information Borrowing Platform Design for Testing Drug Candidates of COVID-19. CANADIAN JOURNAL OF INFECTIOUS DISEASES AND MEDICAL MICROBIOLOGY 2022; 2022:9293681. [PMID: 35462681 PMCID: PMC9029212 DOI: 10.1155/2022/9293681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/16/2022] [Indexed: 12/15/2022]
Abstract
Background There have been thousands of clinical trials for COVID-19 to target effective treatments. However, quite a few of them are traditional randomized controlled trials with low efficiency. Considering the three particularities of pandemic disease: timeliness, repurposing, and case spike, new trial designs need to be developed to accelerate drug discovery. Methods We propose an adaptive information borrowing platform design that can sequentially test drug candidates under a unified framework with early efficacy/futility stopping. Power prior is used to borrow information from previous stages and the time trend calibration method deals with the baseline effectiveness drift. Two drug development strategies are applied: the comprehensive screening strategy and the optimal screening strategy. At the same time, we adopt adaptive randomization to set a higher allocation ratio to the experimental arms for ethical considerations, which can help more patients to receive the latest treatments and shorten the trial duration. Results Simulation shows that in general, our method has great operating characteristics with type I error controlled and power increased, which can select effective/optimal drugs with a high probability. The early stopping rules can be successfully triggered to stop the trial when drugs are either truly effective or not optimal, and the time trend calibration performs consistently well with regard to different baseline drifts. Compared with the nonborrowing method, borrowing information in the design substantially improves the probability of screening promising drugs and saves the sample size. Sensitivity analysis shows that our design is robust to different design parameters. Conclusions Our proposed design achieves the goal of gaining efficiency, saving sample size, meeting ethical requirements, and speeding up the trial process and is suitable and well performed for COVID-19 clinical trials to screen promising treatments or target optimal therapies.
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32
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Ohigashi T, Maruo K, Sozu T, Gosho M. Using horseshoe prior for incorporating multiple historical control data in randomized controlled trials. Stat Methods Med Res 2022; 31:1392-1404. [PMID: 35379046 DOI: 10.1177/09622802221090752] [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: 11/16/2022]
Abstract
Meta-analytic approaches and power priors are often used to incorporate historical controls into the analysis of a current randomized controlled trial. In this study, we propose a method for incorporating multiple historical controls based on a horseshoe prior, which is a type of global-local shrinkage prior. The method assumes that historical controls follow the same distribution as the current control. In the case in which only a few historical controls are heterogeneous, we consider them to follow a potentially biased distribution from the distribution of the current control. We analyze two clinical trial examples with binary and time-to-event endpoints and conduct simulation studies to compare the performance of the proposed and existing methods. In the analysis of the clinical trial example, the posterior standard deviation of the treatment effect is decreased by the proposed method by considering the bias between the current control and heterogeneous historical control. In the scenarios in which the current and historical controls follow the same distribution, the statistical power using the proposed method is higher than that using existing methods. The proposed method is advantageous when few or no heterogeneous historical controls are expected.
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Affiliation(s)
- Tomohiro Ohigashi
- Graduate School of Comprehensive Human Sciences, 13121University of Tsukuba, Tsukuba, Japan.,Department of Biostatistics, Tsukuba Clinical Research & Development Organization, 13121University of Tsukuba, Tsukuba, Japan
| | - Kazushi Maruo
- Department of Biostatistics, Faculty of Medicine, 13121University of Tsukuba, Tsukuba, Japan
| | - Takashi Sozu
- Department of Information and Computer Technology, Faculty of Engineering, 26413Tokyo University of Science, Tokyo, Japan
| | - Masahiko Gosho
- Department of Biostatistics, Faculty of Medicine, 13121University of Tsukuba, Tsukuba, Japan
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33
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Zhang L, Wang Z, Wang L, Cui L, Sokolove J, Chan I. A Simple Approach to Incorporating Historical Control Data in Clinical Trial Design and Analysis. STATISTICS IN BIOSCIENCES 2022. [DOI: 10.1007/s12561-022-09342-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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34
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Sawamoto R, Oba K, Matsuyama Y. Bayesian adaptive randomization design incorporating propensity score-matched historical controls. Pharm Stat 2022; 21:1074-1089. [PMID: 35278032 DOI: 10.1002/pst.2203] [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: 03/30/2021] [Revised: 02/28/2022] [Accepted: 02/28/2022] [Indexed: 11/09/2022]
Abstract
Incorporating historical control data to augment the control arm in randomized controlled trials (RCTs) is one way of increasing their efficiency and feasibility when adequate RCTs cannot be conducted. In recent work, a Bayesian adaptive randomization design incorporating historical control data has been proposed to reduce sample size according to the amount of information that could be borrowed, assessed at interim assessment in respect to prior-data conflict. However, the approach does not distinguish between the two sources of prior-data conflict: (1) imbalance in measured covariates, and (2) imbalance in unmeasured covariates. In this paper, we propose an extension of the Bayesian adaptive randomization design to incorporate propensity score-matched historical controls. At interim assessment, historical controls similar to the concurrent controls in terms of measured covariates are selected using propensity score matching. Then, final sample size of the control arm is adjusted according to the extent of borrowing from the matched historical controls quantified by effective historical sample size. The conditional power prior approach and commensurate prior approach are adopted for designing the prior, and addressing prior-data conflict due to unmeasured covariate imbalance. Simulation results show that the proposed method yields reduced bias in treatment effect estimates, type I error at the nominal level, and reduced sample size while maintaining statistical power. Even when residual imbalance exists due to unmeasured covariates, the proposed method borrowed more information without risking substantially inflated type I error and bias, providing meaningful implications for use of historical controls to facilitate the conduct of adequate RCTs.
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Affiliation(s)
- Ryo Sawamoto
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Koji Oba
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Yutaka Matsuyama
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
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35
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Qi H, Rizopoulos D, Lesaffre E, van Rosmalen J. Incorporating historical controls in clinical trials with longitudinal outcomes using the modified power prior. Pharm Stat 2022; 21:818-834. [PMID: 35128780 PMCID: PMC9356117 DOI: 10.1002/pst.2195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 01/12/2022] [Accepted: 01/22/2022] [Indexed: 11/16/2022]
Abstract
Several dynamic borrowing methods, such as the modified power prior (MPP), the commensurate prior, have been proposed to increase statistical power and reduce the required sample size in clinical trials where comparable historical controls are available. Most methods have focused on cross‐sectional endpoints, and appropriate methodology for longitudinal outcomes is lacking. In this study, we extend the MPP to the linear mixed model (LMM). An important question is whether the MPP should use the conditional version of the LMM (given the random effects) or the marginal version (averaged over the distribution of the random effects), which we refer to as the conditional MPP and the marginal MPP, respectively. We evaluated the MPP for one historical control arm via a simulation study and an analysis of the data of Alzheimer's Disease Cooperative Study (ADCS) with the commensurate prior as the comparator. The conditional MPP led to inflated type I error rate when there existed moderate or high between‐study heterogeneity. The marginal MPP and the commensurate prior yielded a power gain (3.6%–10.4% vs. 0.6%–4.6%) with the type I error rates close to 5% (5.2%–6.2% vs. 3.8%–6.2%) when the between‐study heterogeneity is not excessively high. For the ADCS data, all the borrowing methods improved the precision of estimates and provided the same clinical conclusions. The marginal MPP and the commensurate prior are useful for borrowing historical controls in longitudinal data analysis, while the conditional MPP is not recommended due to inflated type I error rates.
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Affiliation(s)
- Hongchao Qi
- Department of BiostatisticsErasmus University Medical CenterRotterdamThe Netherlands
- Department of EpidemiologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Dimitris Rizopoulos
- Department of BiostatisticsErasmus University Medical CenterRotterdamThe Netherlands
- Department of EpidemiologyErasmus University Medical CenterRotterdamThe Netherlands
| | | | - Joost van Rosmalen
- Department of BiostatisticsErasmus University Medical CenterRotterdamThe Netherlands
- Department of EpidemiologyErasmus University Medical CenterRotterdamThe Netherlands
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36
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Faya P, Novick S, Seaman JW, Peterson JJ, Pourmohamad T, Banton D, Zheng Y. Continuous method validation: beyond one-time studies to characterize analytical methods. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2036637] [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)
- Paul Faya
- Statistics – Discovery/Development, Eli Lilly and Company, Indianapolis, IN, USA
| | - Steven Novick
- Department of Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Gaithersburg, MD, USA
| | - John W. Seaman
- Department of Statistical Science, Baylor University, Waco, TX, USA
| | | | | | - Dwaine Banton
- Translational Medicine and Early Development Statistics, Janssen, Raritan, NJ, USA
| | - Yanbing Zheng
- Data and Statistical Sciences, AbbVie Inc., North Chicago, IL, USA
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37
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Li H, Tiwari R, Li QH. [RWD] conditional borrowing external data to establish a hybrid control arm in randomized clinical trials. J Biopharm Stat 2022; 32:954-968. [DOI: 10.1080/10543406.2021.2021227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Hongfei Li
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, New Jersey, USA
- Department of Statistics, University of Connecticut, Storrs, Connecticut, USA
| | - Ram Tiwari
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, New Jersey, USA
| | - Qian H. Li
- Department of Biostatistics and Programming Statistics & Data Corporation, Tempe, Arizona, USA
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38
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Wang X, Suttner L, Jemielita T, Li X. Propensity score-integrated Bayesian prior approaches for augmented control designs: a simulation study. J Biopharm Stat 2021; 32:170-190. [PMID: 34939894 DOI: 10.1080/10543406.2021.2011743] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Drug development can be costly, and the availability of clinical trial participants may be limited either due to the disease setting (rare or pediatric diseases) or due to many sponsors evaluating multiple drugs or combinations in the same patient population. To maximize resource utilization, sponsors may leverage patient-level control data from historical trials. However, in a study with no control arm, it is impossible to evaluate if the historical controls are an appropriate comparator for the current study. Here, instead of conducting a single-arm trial and relying solely on historical controls, we evaluate the situation where a minimal number of patients are enrolled into a control arm, which is augmented by borrowing historical control data. Propensity score (PS) methods are commonly used to minimize bias for non-randomized data. In addition, Bayesian information borrowing with PS adjustments has been proposed when it may not be reasonable to include all available historical data. This paper proposes using PS adjustment integrated with Bayesian commensurate priors to adaptively borrow information. We then evaluate the performance of different PS adjustment methods and different Bayesian priors for augmented control using simulation studies to help inform the design of future trials. In general, we find that propensity weighting or matching combined with the commensurate prior yield reasonable statistical properties across a range of scenarios. Finally, our proposed methods are applied to a real trial with a binary outcome.Abbreviations: PS: propensity score; IPTW: inverse probability of treatment weighting; ATT: average treatment effect on those who received treatment; RCT: randomized controlled trial; CDD: covariate distribution difference; ESS: effective sample size.
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Affiliation(s)
- Xi Wang
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania, USA
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39
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Zhou T, Ji Y. Incorporating external data into the analysis of clinical trials via Bayesian additive regression trees. Stat Med 2021; 40:6421-6442. [PMID: 34494288 DOI: 10.1002/sim.9191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/18/2021] [Accepted: 08/21/2021] [Indexed: 11/06/2022]
Abstract
Most clinical trials involve the comparison of a new treatment to a control arm (eg, the standard of care) and the estimation of a treatment effect. External data, including historical clinical trial data and real-world observational data, are commonly available for the control arm. With proper statistical adjustments, borrowing information from external data can potentially reduce the mean squared errors of treatment effect estimates and increase the power of detecting a meaningful treatment effect. In this article, we propose to use Bayesian additive regression trees (BART) for incorporating external data into the analysis of clinical trials, with a specific goal of estimating the conditional or population average treatment effect. BART naturally adjusts for patient-level covariates and captures potentially heterogeneous treatment effects across different data sources, achieving flexible borrowing. Simulation studies demonstrate that BART maintains desirable and robust performance across a variety of scenarios and compares favorably to alternatives. We illustrate the proposed method with an acupuncture trial and a colorectal cancer trial.
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Affiliation(s)
- Tianjian Zhou
- Department of Statistics, Colorado State University, Fort Collins, Colorado, USA
| | - Yuan Ji
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
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40
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Wang Z, Lin L, Murray T, Hodges JS, Chu H. BRIDGING RANDOMIZED CONTROLLED TRIALS AND SINGLE-ARM TRIALS USING COMMENSURATE PRIORS IN ARM-BASED NETWORK META-ANALYSIS. Ann Appl Stat 2021; 15:1767-1787. [PMID: 36032933 PMCID: PMC9417056 DOI: 10.1214/21-aoas1469] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Network meta-analysis (NMA) is a powerful tool to compare multiple treatments directly and indirectly by combining and contrasting multiple independent clinical trials. Because many NMAs collect only a few eligible randomized controlled trials (RCTs), there is an urgent need to synthesize different sources of information, e.g., from both RCTs and single-arm trials. However, single-arm trials and RCTs may have different populations and quality, so that assuming they are exchangeable may be inappropriate. This article presents a novel method using a commensurate prior on variance (CPV) to borrow variance (rather than mean) information from single-arm trials in an arm-based (AB) Bayesian NMA. We illustrate the advantages of this CPV method by reanalyzing an NMA of immune checkpoint inhibitors in cancer patients. Comprehensive simulations investigate the impact on statistical inference of including single-arm trials. The simulation results show that the CPV method provides efficient and robust estimation even when the two sources of information are moderately inconsistent.
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Affiliation(s)
- Zhenxun Wang
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Lifeng Lin
- Department of Statistics, Florida State University, Tallahassee, FL 32306, USA
| | - Thomas Murray
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - James S Hodges
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Haitao Chu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
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41
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Ling SX, Hobbs BP, Kaizer AM, Koopmeiners JS. Calibrated dynamic borrowing using capping priors. J Biopharm Stat 2021; 31:852-867. [PMID: 35129422 PMCID: PMC9940118 DOI: 10.1080/10543406.2021.1998100] [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: 02/09/2023]
Abstract
Multisource exchangeability models (MEMs), a BayeTsian approach for dynamically integrating information from multiple clinical trials, are a promising approach for gaining efficiency in randomized controlled trials. When the supplementary trials are considerably larger than the primary trial, care must be taken when integrating supplementary data to avoid overwhelming the primary trial. In this paper, we propose "capping priors," which controls the extent of dynamic borrowing by placing an a priori cap on the effective supplemental sample size. We demonstrate the behavior of this technique via simulation, and apply our method to four randomized trials of very low nicotine content cigarettes.
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Affiliation(s)
- Sharon X. Ling
- Division of Biostatistics, School of Public Health, University of Minnesota
| | | | - Alexander M. Kaizer
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus
| | - Joseph S. Koopmeiners
- Division of Biostatistics, School of Public Health, University of Minnesota,Correspondence
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42
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Kotalik A, Vock DM. Dynamic borrowing in the presence of treatment effect heterogeneity. Biostatistics 2021; 22:789-804. [PMID: 31977040 PMCID: PMC8511947 DOI: 10.1093/biostatistics/kxz066] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 12/16/2019] [Accepted: 12/22/2019] [Indexed: 11/13/2022] Open
Abstract
A number of statistical approaches have been proposed for incorporating supplemental information in randomized clinical trials. Existing methods often compare the marginal treatment effects to evaluate the degree of consistency between sources. Dissimilar marginal treatment effects would either lead to increased bias or down-weighting of the supplemental data. This represents a limitation in the presence of treatment effect heterogeneity, in which case the marginal treatment effect may differ between the sources solely due to differences between the study populations. We introduce the concept of covariate-adjusted exchangeability, in which differences in the marginal treatment effect can be explained by differences in the distributions of the effect modifiers. The potential outcomes framework is used to conceptualize covariate-adjusted and marginal exchangeability. We utilize a linear model and the existing multisource exchangeability models framework to facilitate borrowing when marginal treatment effects are dissimilar but covariate-adjusted exchangeability holds. We investigate the operating characteristics of our method using simulations. We also illustrate our method using data from two clinical trials of very low nicotine content cigarettes. Our method has the ability to incorporate supplemental information in a wider variety of situations than when only marginal exchangeability is considered.
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Affiliation(s)
- Ales Kotalik
- Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. SE, Minneapolis, MN 55455, USA
| | - David M Vock
- Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St. SE, Minneapolis, MN 55455, USA
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43
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Yuan W, Chen MH, Zhong J. Flexible Conditional Borrowing Approaches for Leveraging Historical Data in the Bayesian Design of Superiority Trials. STATISTICS IN BIOSCIENCES 2021. [DOI: 10.1007/s12561-021-09321-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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44
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Murray TA, Thall PF, Schortgen F, Asfar P, Zohar S, Katsahian S. Robust Adaptive Incorporation of Historical Control Data in a Randomized Trial of External Cooling to Treat Septic Shock. BAYESIAN ANALYSIS 2021; 16:825-844. [PMID: 36277025 PMCID: PMC9585618 DOI: 10.1214/20-ba1229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This paper proposes randomized controlled clinical trial design to evaluate external cooling as a means to control fever and thereby reduce mortality in patients with septic shock. The trial will include concurrent external cooling and control arms while adaptively incorporating historical control arm data. Bayesian group sequential monitoring will be done using a posterior comparative test based on the 60-day survival distribution in each concurrent arm. Posterior inference will follow from a Bayesian discrete time survival model that facilitates adaptive incorporation of the historical control data through an innovative regression framework with a multivariate spike-and-slab prior distribution on the historical bias parameters. For each interim test, the amount of information borrowed from the historical control data will be determined adaptively in a manner that reflects the degree of agreement between historical and concurrent control arm data. Guidance is provided for selecting Bayesian posterior probability group-sequential monitoring boundaries. Simulation results elucidating how the proposed method borrows strength from the historical control data are reported. In the absence of historical control arm bias, the proposed design controls the type I error rate and provides substantially larger power than reasonable comparators, whereas in the presence bias of varying magnitude, type I error rate inflation is curbed.
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Affiliation(s)
- Thomas A Murray
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
- Funded in part by NIH/NCI Grant P30-CA077598. Thanks to Medtronic Inc. for their support in the form of a Faculty Fellowship
| | - Peter F Thall
- Department of Biostatistics, M. D. Anderson Cancer Center, Houston, TX, USA
- Funded in part by NIH/NCI Grant 5-R01-CA083932
| | - Frederique Schortgen
- Service of Intensive Care Unit, Hôspital Intercommunal de Créteil, Créteil, France
| | - Pierre Asfar
- Service of medical Intensive care and hyperbaric oxygen therapy unit, Centre Hospitalier Universitaire Angers, Angers, France
- Laboratoire de Biologie Neurovasculaire et Mitochondriale Intégrée, CNRS UMR 6214 - Inserm U1083, Université Angers, UBL, Angers, France
| | - Sarah Zohar
- Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
- Katsahian S. and Zohar S. have equally contributed to this paper
| | - Sandrine Katsahian
- Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
- CIC-EC 1418 Inserm, Hôpital Européen Georges-Pompidou, Paris, France
- Katsahian S. and Zohar S. have equally contributed to this paper
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45
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Gray CM, Grimson F, Layton D, Pocock S, Kim J. A Framework for Methodological Choice and Evidence Assessment for Studies Using External Comparators from Real-World Data. Drug Saf 2021; 43:623-633. [PMID: 32440847 PMCID: PMC7305259 DOI: 10.1007/s40264-020-00944-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Several approaches have been proposed recently to accelerate the pathway from drug discovery to patient access. These include novel designs such as using controls external to the clinical trial where standard randomised controls are not feasible. In parallel, there has been rapid growth in the application of routinely collected healthcare ‘real-world’ data for post-market safety and effectiveness studies. Thus, using real-world data to establish an external comparator arm in clinical trials is a natural next step. Regulatory authorities have begun to endorse the use of external comparators in certain circumstances, with some positive outcomes for new drug approvals. Given the potential to introduce bias associated with observational studies, there is a need for recommendations on how external comparators should be best used. In this article, we propose an evaluation framework for real-world data external comparator studies that enables full assessment of available evidence and related bias. We define the principle of exchangeability and discuss the applicability of criteria described by Pocock for consideration of the exchangeability of the external and trial populations. We explore how trial designs using real-world data external comparators fit within the evidence hierarchy and propose a four-step process for good conduct of external comparator studies. This process is intended to maximise the quality of evidence based on careful study design and the combination of covariate balancing, bias analysis and combining outcomes.
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Affiliation(s)
- Christen M Gray
- EMEA Centre of Excellence for Retrospective Studies, IQVIA, London, UK.
| | - Fiona Grimson
- EMEA Centre of Excellence for Retrospective Studies, IQVIA, London, UK
| | - Deborah Layton
- EMEA Centre of Excellence for Retrospective Studies, IQVIA, London, UK.,School of Pharmacy and Bioengineering, Keele University, Staffordshire, UK.,School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, UK
| | - Stuart Pocock
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Joseph Kim
- EMEA Centre of Excellence for Retrospective Studies, IQVIA, London, UK.,Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.,School of Pharmacy, University College London, London, UK
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46
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Spahr A, Rosli Z, Legault M, Tran LT, Fournier S, Toutounchi H, Darbelli L, Madjar C, Lucia C, St-Jean ML, Das S, Evans AC, Bernard G. The LORIS MyeliNeuroGene rare disease database for natural history studies and clinical trial readiness. Orphanet J Rare Dis 2021; 16:328. [PMID: 34301277 PMCID: PMC8299589 DOI: 10.1186/s13023-021-01953-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/11/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Rare diseases are estimated to affect 150-350 million people worldwide. With advances in next generation sequencing, the number of known disease-causing genes has increased significantly, opening the door for therapy development. Rare disease research has therefore pivoted from gene discovery to the exploration of potential therapies. With impending clinical trials on the horizon, researchers are in urgent need of natural history studies to help them identify surrogate markers, validate outcome measures, define historical control patients, and design therapeutic trials. RESULTS We customized a browser-accessible multi-modal (e.g. genetics, imaging, behavioral, patient-determined outcomes) database to increase cohort sizes, identify surrogate markers, and foster international collaborations. Ninety data entry forms were developed including family, perinatal, developmental history, clinical examinations, diagnostic investigations, neurological evaluations (i.e. spasticity, dystonia, ataxia, etc.), disability measures, parental stress, and quality of life. A customizable clinical letter generator was created to assist in continuity of patient care. CONCLUSIONS Small cohorts and underpowered studies are a major challenge for rare disease research. This online, rare disease database will be accessible from all over the world, making it easier to share and disseminate data. We have outlined the methodology to become Title 21 Code of Federal Regulations Part 11 Compliant, which is a requirement to use electronic records as historical controls in clinical trials in the United States. Food and Drug Administration compliant databases will be life-changing for patients and families when historical control data is used for emerging clinical trials. Future work will leverage these tools to delineate the natural history of several rare diseases and we are confident that this database will be used on a larger scale to improve care for patients affected with rare diseases.
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Affiliation(s)
- Aaron Spahr
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Zaliqa Rosli
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Mélanie Legault
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Luan T Tran
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Simon Fournier
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Helia Toutounchi
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Lama Darbelli
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Cécile Madjar
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Cassandra Lucia
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Marie-Lou St-Jean
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Department of Pediatrics, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada
| | - Samir Das
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Geneviève Bernard
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
- Department of Pediatrics, McGill University, Montréal, Québec, Canada.
- Department of Human Genetics, McGill University, Montréal, Québec, Canada.
- Department of Specialized Medicine, Division of Medical Genetics, McGill University Health Centre, Montréal, Québec, Canada.
- Child Health and Human Development Program, Research Institute, McGill University Health Center, Montréal, Québec, Canada.
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47
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Zhou Y, Lin R, Lee JJ. The use of local and nonlocal priors in Bayesian test-based monitoring for single-arm phase II clinical trials. Pharm Stat 2021; 20:1183-1199. [PMID: 34008317 DOI: 10.1002/pst.2139] [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: 05/21/2020] [Revised: 03/24/2021] [Accepted: 05/01/2021] [Indexed: 11/10/2022]
Abstract
Bayesian sequential monitoring is widely used in adaptive phase II studies where the objective is to examine whether an experimental drug is efficacious. Common approaches for Bayesian sequential monitoring are based on posterior or predictive probabilities and Bayesian hypothesis testing procedures using Bayes factors. In the first part of the paper, we briefly show the connections between test-based (TB) and posterior probability-based (PB) sequential monitoring approaches. Next, we extensively investigate the choice of local and nonlocal priors for the TB monitoring procedure. We describe the pros and cons of different priors in terms of operating characteristics. We also develop a user-friendly Shiny application to implement the TB design.
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Affiliation(s)
- Yanhong Zhou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Houstan, Texas, USA
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Houstan, Texas, USA
| | - J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Houstan, Texas, USA
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48
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Bennett M, White S, Best N, Mander A. A novel equivalence probability weighted power prior for using historical control data in an adaptive clinical trial design: A comparison to standard methods. Pharm Stat 2021; 20:462-484. [PMID: 33474798 PMCID: PMC8611797 DOI: 10.1002/pst.2088] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 10/05/2020] [Accepted: 10/14/2020] [Indexed: 11/08/2022]
Abstract
A standard two-arm randomised controlled trial usually compares an intervention to a control treatment with equal numbers of patients randomised to each treatment arm and only data from within the current trial are used to assess the treatment effect. Historical data are used when designing new trials and have recently been considered for use in the analysis when the required number of patients under a standard trial design cannot be achieved. Incorporating historical control data could lead to more efficient trials, reducing the number of controls required in the current study when the historical and current control data agree. However, when the data are inconsistent, there is potential for biased treatment effect estimates, inflated type I error and reduced power. We introduce two novel approaches for binary data which discount historical data based on the agreement with the current trial controls, an equivalence approach and an approach based on tail area probabilities. An adaptive design is used where the allocation ratio is adapted at the interim analysis, randomising fewer patients to control when there is agreement. The historical data are down-weighted in the analysis using the power prior approach with a fixed power. We compare operating characteristics of the proposed design to historical data methods in the literature: the modified power prior; commensurate prior; and robust mixture prior. The equivalence probability weight approach is intuitive and the operating characteristics can be calculated exactly. Furthermore, the equivalence bounds can be chosen to control the maximum possible inflation in type I error.
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Affiliation(s)
- Maxine Bennett
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
| | - Simon White
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Department of PsychiatryUniversity of CambridgeCambridgeUK
| | | | - Adrian Mander
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Centre for Trials ResearchCardiff UniversityCardiffUK
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49
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Hupf B, Bunn V, Lin J, Dong C. Bayesian semiparametric meta-analytic-predictive prior for historical control borrowing in clinical trials. Stat Med 2021; 40:3385-3399. [PMID: 33851441 DOI: 10.1002/sim.8970] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 12/22/2022]
Abstract
When designing a clinical trial, borrowing historical control information can provide a more efficient approach by reducing the necessary control arm sample size while still yielding increased power. Several Bayesian methods for incorporating historical information via a prior distribution have been proposed, for example, (modified) power prior, (robust) meta-analytic predictive prior. When utilizing historical control borrowing, the prior parameter(s) must be specified to determine the magnitude of borrowing before the current data are observed. Thus, a flexible prior is needed in case of heterogeneity between historic trials or prior data conflict with the current trial. To incorporate the ability to selectively borrow historic information, we propose a Bayesian semiparametric meta-analytic-predictive prior. Using a Dirichlet process mixture prior allows for relaxation of parametric assumptions, and lets the model adaptively learn the relationship between the historic and current control data. Additionally, we generalize a method for estimating the prior effective sample size (ESS) for the proposed prior. This gives an intuitive quantification of the amount of information borrowed from historical trials, and aids in tuning the prior to the specific task at hand. We illustrate the effectiveness of the proposed methodology by comparing performance between existing methods in an extensive simulation study and a phase II proof-of-concept trial in ankylosing spondylitis. In summary, our proposed robustification of the meta-analytic-predictive prior alleviates the need for prespecifying the amount of borrowing, providing a more flexible and robust method to integrate historical data from multiple study sources in the design and analysis of clinical trials.
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Affiliation(s)
- Bradley Hupf
- Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Veronica Bunn
- Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Jianchang Lin
- Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Cheng Dong
- Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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50
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Kunz CU, Jörgens S, Bretz F, Stallard N, Van Lancker K, Xi D, Zohar S, Gerlinger C, Friede T. Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue? Stat Biopharm Res 2020; 12:461-477. [PMID: 34191979 PMCID: PMC8011492 DOI: 10.1080/19466315.2020.1799857] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/17/2020] [Accepted: 07/18/2020] [Indexed: 01/09/2023]
Abstract
Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.
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Affiliation(s)
| | | | - Frank Bretz
- Novartis Pharma AG, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- Division of Health Sciences, Warwick Medical School, The University of Warwick, Coventry, UK
| | - Kelly Van Lancker
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Dong Xi
- Novartis Pharmaceuticals, East Hanover, NJ
| | - Sarah Zohar
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Christoph Gerlinger
- Statistics and Data Insights, Bayer AG, Berlin, Germany
- Department of Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, Homburg/Saar, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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