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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 rec...

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Autores principales: Palmer, Stephen, Lin, Yi, Martin, Thomas G., Jagannath, Sundar, Jakubowiak, Andrzej, Usmani, Saad Z., Buyukkaramikli, Nasuh, Phelps, Hilary, Slowik, Rafal, Pan, Feng, Valluri, Satish, Pacaud, Lida, Jackson, Graham
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Healthcare 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10447673/
https://www.ncbi.nlm.nih.gov/pubmed/37270762
http://dx.doi.org/10.1007/s40487-023-00230-x
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author Palmer, Stephen
Lin, Yi
Martin, Thomas G.
Jagannath, Sundar
Jakubowiak, Andrzej
Usmani, Saad Z.
Buyukkaramikli, Nasuh
Phelps, Hilary
Slowik, Rafal
Pan, Feng
Valluri, Satish
Pacaud, Lida
Jackson, Graham
author_facet Palmer, Stephen
Lin, Yi
Martin, Thomas G.
Jagannath, Sundar
Jakubowiak, Andrzej
Usmani, Saad Z.
Buyukkaramikli, Nasuh
Phelps, Hilary
Slowik, Rafal
Pan, Feng
Valluri, Satish
Pacaud, Lida
Jackson, Graham
author_sort Palmer, Stephen
collection PubMed
description 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.
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spelling pubmed-104476732023-08-25 Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma Palmer, Stephen Lin, Yi Martin, Thomas G. Jagannath, Sundar Jakubowiak, Andrzej Usmani, Saad Z. Buyukkaramikli, Nasuh Phelps, Hilary Slowik, Rafal Pan, Feng Valluri, Satish Pacaud, Lida Jackson, Graham Oncol Ther Original Research 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. Springer Healthcare 2023-06-04 /pmc/articles/PMC10447673/ /pubmed/37270762 http://dx.doi.org/10.1007/s40487-023-00230-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research
Palmer, Stephen
Lin, Yi
Martin, Thomas G.
Jagannath, Sundar
Jakubowiak, Andrzej
Usmani, Saad Z.
Buyukkaramikli, Nasuh
Phelps, Hilary
Slowik, Rafal
Pan, Feng
Valluri, Satish
Pacaud, Lida
Jackson, Graham
Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma
title Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma
title_full Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma
title_fullStr Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma
title_full_unstemmed Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma
title_short Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma
title_sort extrapolation of survival data using a bayesian approach: a case study leveraging external data from cilta-cel therapy in multiple myeloma
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10447673/
https://www.ncbi.nlm.nih.gov/pubmed/37270762
http://dx.doi.org/10.1007/s40487-023-00230-x
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