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Maher TM, Brown KK, Cunningham S, DeBoer EM, Deterding R, Fiorino EK, Griese M, Schwerk N, Warburton D, Young LR, Gahlemann M, Voss F, Stock C. Estimating the effect of nintedanib on forced vital capacity in children and adolescents with fibrosing interstitial lung disease using a Bayesian dynamic borrowing approach. Pediatr Pulmonol 2024. [PMID: 38289091 DOI: 10.1002/ppul.26882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/15/2023] [Accepted: 01/10/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND The rarity of childhood interstitial lung disease (chILD) makes it challenging to conduct powered trials. In the InPedILD trial, among 39 children and adolescents with fibrosing ILD, there was a numerical benefit of nintedanib versus placebo on change in forced vital capacity (FVC) over 24 weeks (difference in mean change in FVC % predicted of 1.21 [95% confidence interval: -3.40, 5.81]). Nintedanib has shown a consistent effect on FVC across populations of adults with different diagnoses of fibrosing ILD. METHODS In a Bayesian dynamic borrowing analysis, prespecified before data unblinding, we incorporated data on the effect of nintedanib in adults and the data from the InPedILD trial to estimate the effect of nintedanib on FVC in children and adolescents with fibrosing ILD. The data from adults were represented as a meta-analytic predictive (MAP) prior distribution with mean 1.69 (95% credible interval: 0.49, 3.08). The adult data were weighted according to expert judgment on their relevance to the efficacy of nintedanib in chILD, obtained in a formal elicitation exercise. RESULTS Combined data from the MAP prior and InPedILD trial analyzed within the Bayesian framework resulted in a median difference between nintedanib and placebo in change in FVC % predicted at Week 24 of 1.63 (95% credible interval: -0.69, 3.40). The posterior probability for superiority of nintedanib versus placebo was 95.5%, reaching the predefined success criterion of at least 90%. CONCLUSION These findings, together with the safety data from the InPedILD trial, support the use of nintedanib in children and adolescents with fibrosing ILDs.
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Affiliation(s)
- Toby M Maher
- Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Kevin K Brown
- Department of Medicine, National Jewish Health, Denver, Colorado, USA
| | - Steven Cunningham
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
| | - Emily M DeBoer
- Section of Pediatric Pulmonary and Sleep Medicine, Department of Pediatrics, University of Colorado Denver, Denver, Colorado, USA
- The Children's Hospital Colorado, Aurora, Colorado, USA
| | - Robin Deterding
- Section of Pediatric Pulmonary and Sleep Medicine, Department of Pediatrics, University of Colorado Denver, Denver, Colorado, USA
- The Children's Hospital Colorado, Aurora, Colorado, USA
| | - Elizabeth K Fiorino
- Departments of Science Education and Pediatrics, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA
| | - Matthias Griese
- Hauner Children's Hospital, German Center for Lung Research (DZL), Ludwig Maximilians University, Munich, Germany
| | - Nicolaus Schwerk
- Clinic for Pediatric Pulmonology, Allergology and Neonatology, Hannover Medical School, Hannover, Germany
| | - David Warburton
- Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Children's Hospital Los Angeles, Los Angeles, California, USA
| | - Lisa R Young
- Division of Pulmonary and Sleep Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | | | - Florian Voss
- Boehringer Ingelheim Pharma GmbH & Co. KG, Ingelheim am Rhein, Germany
| | - Christian Stock
- Boehringer Ingelheim Pharma GmbH & Co. KG, Ingelheim am Rhein, Germany
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2
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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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3
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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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4
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Lin J, Liao R, Gamalo-Siebers M. Dynamic incorporation of real world evidence within the framework of adaptive design. J Biopharm Stat 2022; 32:986-998. [PMID: 35730907 DOI: 10.1080/10543406.2022.2089159] [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: 10/17/2022]
Abstract
For the clinical studies in rare diseases or small patient populations, having an adequately powered randomized controlled trial is further complicated by variability. As such, sample size re-estimation can be a useful tool if at an interim look the trial sample size needs to be increased to achieve adequate power to reject the null hypothesis. Meanwhile, borrowing or extrapolating information from real-world data or real-world evidence has gained increasing use in trial design and analysis since 2014. Combining these two strategies, high-quality real-world data, if leveraged properly, has the potential to generate real-world evidence that can assist interim decision-making, lower enrollment burden, and reduce study timeline and costs. With proper borrowing from historical control, some of the challenges in these high unmet medical need studies could be resolved considerably. We examine the incorporation of real-world evidence within the framework of adaptive design strategy in pediatric type II diabetes trials where recruitment has been challenging and the completion is hardly on time. Simulations under various scenarios are conducted to assess the borrowing strategy, i.e., the matching method in combination of sample size re-estimation. Comparisons of performance metrics are presented to showcase the advantages of proposed method.
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Affiliation(s)
- Junjing Lin
- Statistics and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Ran Liao
- Statistics, Eli Lilly and Co Ltd, Basingstoke, UK
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5
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Extrapolation as a Default Strategy in Pediatric Drug Development. Ther Innov Regul Sci 2022; 56:883-894. [PMID: 35006587 DOI: 10.1007/s43441-021-00367-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 12/20/2021] [Indexed: 02/06/2023]
Abstract
Pediatric drug development lags adult development by about 8 years (Mulugeta et al. in Pediatr Clin 64(6):1185-1196, 2017). In such context, many incentives, regulations, and innovative techniques have been proposed to address the disparity for pediatric patients. One such strategy is extrapolation of efficacy from a reference population. Extrapolation is currently justified by providing evidence in support of the effective use of drugs in children when the course of the disease and the expected treatment response would be sufficiently similar in the pediatric and reference population. This paper's position is that, despite uncertainties, pediatric drug development programs should initially assume some degree of extrapolation. The degree to which extrapolation can be used lies along a continuum representing the uncertainties to be addressed through generation of new pediatric evidence. In addressing these uncertainties, the extrapolation strategy should reflect the level of tolerable uncertainty concerning the decision to expose a child to the risks of a new drug. This judgment about the level of tolerable uncertainty should vary with the context (e.g., disease severity, existing therapeutic options) and can be embedded into pediatric drug development archetypes to ascertain the extent of studies needed and whether simultaneous development for adults and adolescents be considered.
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Psioda MA, Xue X. A BAYESIAN ADAPTIVE TWO-STAGE DESIGN FOR PEDIATRIC CLINICAL TRIALS. J Biopharm Stat 2020; 30:1091-1108. [DOI: 10.1080/10543406.2020.1821704] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Matthew A. Psioda
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Xiaoqiang Xue
- Center for Statistics of Drug Development, Data Science Safety and Regulatory, IQVIA Inc., Durham, NC, USA
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Conklin LS, Hoffman EP, van den Anker J. Developmental Pharmacodynamics and Modeling in Pediatric Drug Development. J Clin Pharmacol 2020; 59 Suppl 1:S87-S94. [PMID: 31502687 DOI: 10.1002/jcph.1482] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 06/17/2019] [Indexed: 12/14/2022]
Abstract
Challenges in pediatric drug development include small patient numbers, limited outcomes research, ethical barriers, and sparse biosamples. Increasingly, pediatric drug development is focusing on extrapolation: leveraging knowledge about adult disease and drug responses to inform projections of drug and clinical trial performance in pediatric subpopulations. Pharmacokinetic-pharmacodynamic (PK-PD) modeling and extrapolation aim to reduce the numbers of patients and data points needed to establish efficacy. Planning for PK-PD and biomarker studies should begin early in the adult drug development program. Extrapolation relies on the assumption that both the underlying disease and the mechanism of action of the drug used to treat that disease are similar in adults and pediatric subpopulations. Clearly, developmental changes in PK and PD need to be considered to enhance the quality of PK-PD modeling and, therefore, increase the success of extrapolation. This article focuses on the influence of differences in PD between adults and pediatric subpopulations that are highly relevant for the use of extrapolation.
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Affiliation(s)
- Laurie S Conklin
- Division of Gastroenterology, Hepatology, and Nutrition, Children's National Health System, Washington, DC, USA.,ReveraGen BioPharma, Rockville, MD, USA
| | - Eric P Hoffman
- ReveraGen BioPharma, Rockville, MD, USA.,Binghamton University-SUNY, School of Pharmacy and Pharmaceutical Sciences, Binghamton, NY, USA
| | - John van den Anker
- ReveraGen BioPharma, Rockville, MD, USA.,Division of Clinical Pharmacology, Children's National Health System, Washington, DC, USA
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Nelson RM. The Use of Pediatric Extrapolation to Avoid Unnecessary Pediatric Clinical Trials. THE AMERICAN JOURNAL OF BIOETHICS : AJOB 2020; 20:114-116. [PMID: 32208075 DOI: 10.1080/15265161.2020.1730489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
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Schmidli H, Häring DA, Thomas M, Cassidy A, Weber S, Bretz F. Beyond Randomized Clinical Trials: Use of External Controls. Clin Pharmacol Ther 2019; 107:806-816. [DOI: 10.1002/cpt.1723] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 11/07/2019] [Indexed: 12/30/2022]
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McCune S, Portman RJ. Innovation and Opportunities in Pediatric Therapeutic Development. Ther Innov Regul Sci 2019; 53:564-566. [PMID: 31438727 DOI: 10.1177/2168479019869754] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Susan McCune
- Office of Pediatric Therapeutics, Office of Clinical Policy and Programs, Office of the Commissioner, U.S. Food and Drug Administration
| | - Ronald J Portman
- Pediatric Development, Science and Innovation, Pediatric Center of Excellence, Clinical Development & Analytics, Novartis Pharmaceuticals Corporation
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