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Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations

BACKGROUND: Standard parametric survival models are commonly used to estimate long-term survival in oncology health technology assessments; however, they can inadequately represent the complex pattern of hazard functions or underlying mechanism of action (MoA) of immuno-oncology (IO) treatments. OBJ...

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Autores principales: Ouwens, Mario J. N. M., Mukhopadhyay, Pralay, Zhang, Yiduo, Huang, Min, Latimer, Nicholas, Briggs, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6830404/
https://www.ncbi.nlm.nih.gov/pubmed/31102143
http://dx.doi.org/10.1007/s40273-019-00806-4
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author Ouwens, Mario J. N. M.
Mukhopadhyay, Pralay
Zhang, Yiduo
Huang, Min
Latimer, Nicholas
Briggs, Andrew
author_facet Ouwens, Mario J. N. M.
Mukhopadhyay, Pralay
Zhang, Yiduo
Huang, Min
Latimer, Nicholas
Briggs, Andrew
author_sort Ouwens, Mario J. N. M.
collection PubMed
description BACKGROUND: Standard parametric survival models are commonly used to estimate long-term survival in oncology health technology assessments; however, they can inadequately represent the complex pattern of hazard functions or underlying mechanism of action (MoA) of immuno-oncology (IO) treatments. OBJECTIVE: The aim of this study was to explore methods for extrapolating overall survival (OS) and provide insights on model selection in the context of the underlying MoA of IO treatments. METHODS: Standard parametric, flexible parametric, cure, parametric mixture and landmark models were applied to data from ATLANTIC (NCT02087423; data cut-off [DCO] 3 June 2016). The goodness of fit of each model was compared using the observed survival and hazard functions, together with the plausibility of corresponding model extrapolation beyond the trial period. Extrapolations were compared with updated data from ATLANTIC (DCO 7 November 2017) for validation. RESULTS: A close fit to the observed OS was seen with all models; however, projections beyond the trial period differed. Estimated mean OS differed substantially across models. The cure models provided the best fit for the new DCO. CONCLUSIONS: Standard parametric models fitted to the initial ATLANTIC DCO generally underestimated longer-term OS, compared with the later DCO. Cure, parametric mixture and response-based landmark models predicted that larger proportions of patients with metastatic non-small cell lung cancer receiving IO treatments may experience long-term survival, which was more in keeping with the observed data. Further research using more mature OS data for IO treatments is needed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40273-019-00806-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-68304042019-11-20 Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations Ouwens, Mario J. N. M. Mukhopadhyay, Pralay Zhang, Yiduo Huang, Min Latimer, Nicholas Briggs, Andrew Pharmacoeconomics Original Research Article BACKGROUND: Standard parametric survival models are commonly used to estimate long-term survival in oncology health technology assessments; however, they can inadequately represent the complex pattern of hazard functions or underlying mechanism of action (MoA) of immuno-oncology (IO) treatments. OBJECTIVE: The aim of this study was to explore methods for extrapolating overall survival (OS) and provide insights on model selection in the context of the underlying MoA of IO treatments. METHODS: Standard parametric, flexible parametric, cure, parametric mixture and landmark models were applied to data from ATLANTIC (NCT02087423; data cut-off [DCO] 3 June 2016). The goodness of fit of each model was compared using the observed survival and hazard functions, together with the plausibility of corresponding model extrapolation beyond the trial period. Extrapolations were compared with updated data from ATLANTIC (DCO 7 November 2017) for validation. RESULTS: A close fit to the observed OS was seen with all models; however, projections beyond the trial period differed. Estimated mean OS differed substantially across models. The cure models provided the best fit for the new DCO. CONCLUSIONS: Standard parametric models fitted to the initial ATLANTIC DCO generally underestimated longer-term OS, compared with the later DCO. Cure, parametric mixture and response-based landmark models predicted that larger proportions of patients with metastatic non-small cell lung cancer receiving IO treatments may experience long-term survival, which was more in keeping with the observed data. Further research using more mature OS data for IO treatments is needed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40273-019-00806-4) contains supplementary material, which is available to authorized users. Springer International Publishing 2019-05-18 2019 /pmc/articles/PMC6830404/ /pubmed/31102143 http://dx.doi.org/10.1007/s40273-019-00806-4 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Research Article
Ouwens, Mario J. N. M.
Mukhopadhyay, Pralay
Zhang, Yiduo
Huang, Min
Latimer, Nicholas
Briggs, Andrew
Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations
title Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations
title_full Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations
title_fullStr Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations
title_full_unstemmed Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations
title_short Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations
title_sort estimating lifetime benefits associated with immuno-oncology therapies: challenges and approaches for overall survival extrapolations
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6830404/
https://www.ncbi.nlm.nih.gov/pubmed/31102143
http://dx.doi.org/10.1007/s40273-019-00806-4
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