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Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity
AIM: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH) WMC NMA to inform long-term survival of therapies. MATERIALS & METHODS: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Becaris Publishing Ltd
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288968/ https://www.ncbi.nlm.nih.gov/pubmed/36651607 http://dx.doi.org/10.2217/cer-2022-0159 |
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author | Heeg, Bart Verhoek, Andre Tremblay, Gabriel Harari, Ofir Soltanifar, Mohsen Chu, Haitao Roychoudhury, Satrajit Cappelleri, Joseph C |
author_facet | Heeg, Bart Verhoek, Andre Tremblay, Gabriel Harari, Ofir Soltanifar, Mohsen Chu, Haitao Roychoudhury, Satrajit Cappelleri, Joseph C |
author_sort | Heeg, Bart |
collection | PubMed |
description | AIM: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH) WMC NMA to inform long-term survival of therapies. MATERIALS & METHODS: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used comparing docetaxel with nivolumab, pembrolizumab and atezolizumab. Cure parameters related to a certain treatment class were assumed to share a common distribution. RESULTS: Standard WMC NMA predicted cure rates were 0.03 (0.01; 0.07), 0.18 (0.12; 0.24), 0.07 (0.02; 0.15) and 0.03 (0.00; 0.09) for docetaxel, nivolumab, pembrolizumab and atezolizumab, respectively, with corresponding incremental life years (LY) of 3.11 (1.65; 4.66), 1.06 (0.41; 2.37) and 0.42 (-0.57; 1.68). The Bayesian hierarchical-WMC-NMA rates were 0.06 (0.03; 0.10), 0.17 (0.11; 0.23), 0.12 (0.05; 0.20) and 0.12 (0.03; 0.23), respectively, with incremental LY of 2.35 (1.04; 3.93), 1.67 (0.68; 2.96) and 1.36 (-0.05; 3.64). CONCLUSION: BH-WMC-NMA impacts incremental mean LYs and cost–effectiveness ratios, potentially affecting reimbursement decisions. |
format | Online Article Text |
id | pubmed-10288968 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Becaris Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-102889682023-08-11 Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity Heeg, Bart Verhoek, Andre Tremblay, Gabriel Harari, Ofir Soltanifar, Mohsen Chu, Haitao Roychoudhury, Satrajit Cappelleri, Joseph C J Comp Eff Res Methodology AIM: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH) WMC NMA to inform long-term survival of therapies. MATERIALS & METHODS: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used comparing docetaxel with nivolumab, pembrolizumab and atezolizumab. Cure parameters related to a certain treatment class were assumed to share a common distribution. RESULTS: Standard WMC NMA predicted cure rates were 0.03 (0.01; 0.07), 0.18 (0.12; 0.24), 0.07 (0.02; 0.15) and 0.03 (0.00; 0.09) for docetaxel, nivolumab, pembrolizumab and atezolizumab, respectively, with corresponding incremental life years (LY) of 3.11 (1.65; 4.66), 1.06 (0.41; 2.37) and 0.42 (-0.57; 1.68). The Bayesian hierarchical-WMC-NMA rates were 0.06 (0.03; 0.10), 0.17 (0.11; 0.23), 0.12 (0.05; 0.20) and 0.12 (0.03; 0.23), respectively, with incremental LY of 2.35 (1.04; 3.93), 1.67 (0.68; 2.96) and 1.36 (-0.05; 3.64). CONCLUSION: BH-WMC-NMA impacts incremental mean LYs and cost–effectiveness ratios, potentially affecting reimbursement decisions. Becaris Publishing Ltd 2023-01-18 /pmc/articles/PMC10288968/ /pubmed/36651607 http://dx.doi.org/10.2217/cer-2022-0159 Text en © 2023 Cytel https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
spellingShingle | Methodology Heeg, Bart Verhoek, Andre Tremblay, Gabriel Harari, Ofir Soltanifar, Mohsen Chu, Haitao Roychoudhury, Satrajit Cappelleri, Joseph C Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity |
title | Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity |
title_full | Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity |
title_fullStr | Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity |
title_full_unstemmed | Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity |
title_short | Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity |
title_sort | bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288968/ https://www.ncbi.nlm.nih.gov/pubmed/36651607 http://dx.doi.org/10.2217/cer-2022-0159 |
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