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A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis

Patient-level health economic data collected alongside clinical trials are an important component of the process of technology appraisal. For end-of-life treatments, the modeling of cost-effectiveness data may involve some form of partitioned survival analysis, in which measures of quality of life a...

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Autor principal: Gabrio, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8488644/
https://www.ncbi.nlm.nih.gov/pubmed/34009065
http://dx.doi.org/10.1177/0272989X211012348
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author Gabrio, Andrea
author_facet Gabrio, Andrea
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description Patient-level health economic data collected alongside clinical trials are an important component of the process of technology appraisal. For end-of-life treatments, the modeling of cost-effectiveness data may involve some form of partitioned survival analysis, in which measures of quality of life and survival for pre- and postprogression periods are combined to generate aggregate measures of clinical benefits (e.g., quality-adjusted survival). In addition, resource use data are often collected and costs are calculated for each type of health service (e.g., treatment, hospital, or adverse events costs). A critical problem in these analyses is that effectiveness and cost data present some complexities, such as nonnormality, spikes, and missingness, which should be addressed using appropriate methods to avoid biased results. This article proposes a general Bayesian framework that takes into account the complexities of trial-based partitioned survival cost-utility data to provide more adequate evidence for policy makers. Our approach is motivated by, and applied to, a working example based on data from a trial assessing the cost-effectiveness of a new treatment for patients with advanced non–small-cell lung cancer.
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spelling pubmed-84886442021-10-05 A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis Gabrio, Andrea Med Decis Making Original Research Articles Patient-level health economic data collected alongside clinical trials are an important component of the process of technology appraisal. For end-of-life treatments, the modeling of cost-effectiveness data may involve some form of partitioned survival analysis, in which measures of quality of life and survival for pre- and postprogression periods are combined to generate aggregate measures of clinical benefits (e.g., quality-adjusted survival). In addition, resource use data are often collected and costs are calculated for each type of health service (e.g., treatment, hospital, or adverse events costs). A critical problem in these analyses is that effectiveness and cost data present some complexities, such as nonnormality, spikes, and missingness, which should be addressed using appropriate methods to avoid biased results. This article proposes a general Bayesian framework that takes into account the complexities of trial-based partitioned survival cost-utility data to provide more adequate evidence for policy makers. Our approach is motivated by, and applied to, a working example based on data from a trial assessing the cost-effectiveness of a new treatment for patients with advanced non–small-cell lung cancer. SAGE Publications 2021-05-19 2021-11 /pmc/articles/PMC8488644/ /pubmed/34009065 http://dx.doi.org/10.1177/0272989X211012348 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research Articles
Gabrio, Andrea
A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis
title A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis
title_full A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis
title_fullStr A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis
title_full_unstemmed A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis
title_short A Bayesian Framework for Patient-Level Partitioned Survival Cost-Utility Analysis
title_sort bayesian framework for patient-level partitioned survival cost-utility analysis
topic Original Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8488644/
https://www.ncbi.nlm.nih.gov/pubmed/34009065
http://dx.doi.org/10.1177/0272989X211012348
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