Stephen Palmer, Yi Lin, Thomas G Martin, Sundar Jagannath, Andrzej Jakubowiak, Saad Z Usmani, Nasuh Buyukkaramikli, Hilary Phelps, Rafal Slowik, Feng Pan, Satish Valluri, Lida Pacaud, Graham Jackson
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Using ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T-cell therapy for multiple myeloma, we used a flexible Bayesian approach to demonstrate use of external longer-term data to reduce the uncertainty in long-term extrapolation.</p><p><strong>Methods: </strong>The pivotal CARTITUDE-1 trial (NCT03548207) provided the primary efficacy data for cilta-cel, including a 12-month median follow-up snapshot of OS. Longer-term (48-month median follow-up) survival data from the phase I LEGEND-2 study (NCT03090659) were also available. Twelve-month CARTITUDE-1 OS data were extrapolated in two ways: (1) conventional survival models with standard parametric distributions (uninformed), and (2) Bayesian survival models whose shape prior was informed from 48-month LEGEND-2 data. For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data.</p><p><strong>Results: </strong>Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference.</p><p><strong>Conclusions: </strong>Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. Bayesian models generated a narrower and more plausible range of OS projections from 12-month data that aligned with observed 28-month data.</p><p><strong>Trial registration: </strong>CARTITUDE-1 ClinicalTrials.gov identifier, NCT03548207. 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For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data.</p><p><strong>Results: </strong>Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference.</p><p><strong>Conclusions: </strong>Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. 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Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma.
Introduction: Extrapolating long-term overall survival (OS) from shorter-term clinical trial data is key to health technology assessment in oncology. However, extrapolation using conventional methods is often subject to uncertainty. Using ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T-cell therapy for multiple myeloma, we used a flexible Bayesian approach to demonstrate use of external longer-term data to reduce the uncertainty in long-term extrapolation.
Methods: The pivotal CARTITUDE-1 trial (NCT03548207) provided the primary efficacy data for cilta-cel, including a 12-month median follow-up snapshot of OS. Longer-term (48-month median follow-up) survival data from the phase I LEGEND-2 study (NCT03090659) were also available. Twelve-month CARTITUDE-1 OS data were extrapolated in two ways: (1) conventional survival models with standard parametric distributions (uninformed), and (2) Bayesian survival models whose shape prior was informed from 48-month LEGEND-2 data. For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data.
Results: Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference.
Conclusions: Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. Bayesian models generated a narrower and more plausible range of OS projections from 12-month data that aligned with observed 28-month data.
Trial registration: CARTITUDE-1 ClinicalTrials.gov identifier, NCT03548207. LEGEND-2 ClinicalTrials.gov identifier, NCT03090659, registered retrospectively on 27 March 2017, and ChiCTR-ONH-17012285.
期刊介绍：
Now indexed in PubMed
Aims and Scope
Oncology and Therapy is an international, peer reviewed, rapid-publication (peer review in 2 weeks, published 3–4 weeks from acceptance) journal dedicated to the publication of high-quality pre-clinical, clinical (all phases), observational, real-world, and health outcomes research around the discovery, development, and use of therapeutics and interventions (including devices) across all therapeutic areas. Studies relating to diagnostics and diagnosis, pharmacoeconomics, public health, epidemiology, quality of life, and patient care, management, and education are also encouraged.
The journal is of interest to a broad audience of healthcare professionals and publishes original research, reviews, communications and letters. The journal is read by a global audience and receives submissions from all over the world. Oncology and Therapy will consider all scientifically sound research be it positive, confirmatory or negative data. Submissions are welcomed whether they relate to an international and/or a country-specific audience, something that is crucially important when researchers are trying to target more specific patient populations. This inclusive approach allows the journal to assist in the dissemination of all scientifically and ethically sound research.
Rapid Publication
The journal’s rapid publication timelines aim for a peer review decision within 2 weeks of submission. If an article is accepted it will be published online 3-4 weeks from acceptance. These rapid timelines are achieved through the combination of a dedicated in-house editorial team, who closely manage article workflow, and an extensive Editorial and Advisory Board who assist with rapid peer review. This allows the journal to support the rapid dissemination of research, whilst still providing robust peer review. Combined with the journal’s open access model this allows for the rapid and efficient communication of the latest research and reviews, allowing the advancement of clinical therapies.
Personal Service
The journal’s dedicated in-house editorial team offer a personal “concierge service” meaning that authors will always have a personal point of contact able to update them on the status of their manuscript. The editorial team check all manuscripts to ensure that articles conform to the most recent COPE, GPP and ICMJE publishing guidelines. This supports the publication of ethically sound and transparent research. We also encourage pre-submission enquiries and are always happy to provide a confidential assessment of manuscripts.
Digital features and plain language summaries
Oncology and Therapy offers a range of additional features designed to increase the visibility, readership and educational value of the journal’s content. Each article is accompanied by key summary points, giving a time-efficient overview of the content to a wide readership. Articles may be accompanied by plain language summaries to assist readers who have some knowledge of, but not in-depth expertise in, the area to understand the scientific content and overall implications of the article. The journal also provides the option to include various types of digital features including animated abstracts, video abstracts, slide decks, audio slides, instructional videos, infographics, podcasts and animations. All additional features are peer reviewed to the same high standard as the article itself. If you consider that your paper would benefit from the inclusion of a digital feature, please let us know. Our editorial team are able to create high-quality slide decks and infographics in-house, and video abstracts through our partner Research Square, and would be happy to assist in any way we can. For further information about digital features, please contact the journal editor (see ‘Contact the Journal’ for email address), and see the ‘Guidelines for digital features and plain language summaries’ document under ‘Submission guidelines’.
Preprints
We encourage posting of preprints of primary research manuscripts on preprint servers, authors'' or institutional websites, and open communications between researchers whether on community preprint servers or preprint commenting platforms. Posting of preprints is not considered prior publication and will not jeopardize consideration in our journals.
Please see here for further information on preprint sharing: https://www.springer.com/gp/authors-editors/journal-author/journal-author-helpdesk/submission/1302#c16721550
Peer Review Process
Upon submission, manuscripts are assessed by the editorial team to ensure they fit within the aims and scope of the journal and are also checked for plagiarism. All suitable submissions are then subject to a comprehensive single-blind peer review. Reviewers are selected based on their relevant expertise and publication history in the subject area. The journal has an extensive pool of editorial and advisory board members who have been selected to assist with peer review based on the afore-mentioned criteria.
At least two extensive reviews are required to make the editorial decision, with the exception of some article types such as Commentaries, Editorials and Letters which are generally reviewed by one member of the Editorial Board. Where reviewer recommendations are conflicted, the editorial board will be contacted for further advice and a presiding decision.
Manuscripts are then either accepted, rejected or authors are required to make major or minor revisions (both reviewer comments and editorial comments may need to be addressed). Once a revised manuscript is re-submitted, it is assessed along with the responses to reviewer comments and if it has been adequately revised it will be accepted for publication. Accepted manuscripts are then copyedited and typeset by the production team before online publication. Appeals against decisions following peer review are considered on a case by case basis and should be sent to the journal editor.
Copyright
Oncology and Therapy''s content is published open access under the Creative Commons Attribution-Noncommercial License, which allows users to read, copy, distribute, and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited. The author assigns the exclusive right to any commercial use of the article to Springer. For more information about the Creative Commons Attribution-Noncommercial License, click here: http://creativecommons.org/licenses/by-nc/4.0
Publication Fees
Upon acceptance of an article, authors will be required to pay the mandatory Rapid Service Fee of £3650/€4500/$5100. The journal will consider fee discounts for developing countries and this is decided on a case by case basis.
Open Access
All articles published by Oncology and Therapy are published open access
Contact
For more information about the journal, including pre-submission enquiries, please contact managing editor Lydia Alborn at lydia.alborn@springer.com.
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