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Selection Bias in Real-World Data Studies Used to Support Health Technology Assessments: A Case Study in Metastatic Cancer

Real-world evidence has been increasingly used to support evaluations of emerging therapies. These investigations are often conducted in settings that may not be representative of the underlying population. The purpose of this investigation was to empirically quantify the magnitude of this selection...

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Autores principales: Jarada, Tamer N., O’Sullivan, Dylan E., Brenner, Darren R., Cheung, Winson Y., Boyne, Devon J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955567/
https://www.ncbi.nlm.nih.gov/pubmed/36826112
http://dx.doi.org/10.3390/curroncol30020151
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author Jarada, Tamer N.
O’Sullivan, Dylan E.
Brenner, Darren R.
Cheung, Winson Y.
Boyne, Devon J.
author_facet Jarada, Tamer N.
O’Sullivan, Dylan E.
Brenner, Darren R.
Cheung, Winson Y.
Boyne, Devon J.
author_sort Jarada, Tamer N.
collection PubMed
description Real-world evidence has been increasingly used to support evaluations of emerging therapies. These investigations are often conducted in settings that may not be representative of the underlying population. The purpose of this investigation was to empirically quantify the magnitude of this selection bias. Individuals diagnosed with solid metastatic cancer in Alberta, Canada, between 2010–2019 were identified using the provincial cancer registry for 13 common metastatic sites. Two outcomes used to support oncology reimbursement decisions were examined: the proportion of individuals who initiated systemic therapy and median overall survival (OS). These outcomes were assessed in the entire population and in a subset of individuals who were referred to a medical oncologist. Among the 23,152 individuals in the entire population, 40.8% (95% CI: 40.2–41.4) initiated systemic therapy, and the median OS from diagnosis was 5.4 months (95% CI: 5.3–5.6). Among those who were referred to a medical oncologist (n = 13,372; 57.8%), 67.4% (95% CI: 66.6–68.2) initiated systemic therapy, and the median OS from diagnosis was 11.2 months (95% CI: 10.9–11.5). The magnitude of bias varied by cancer site where lower referral rates were associated with greater bias. Non-referral is an important source of selection bias in real-world investigations. Studies that rely on limited-catchment real-world data should be interpreted with caution, particularly in metastatic cancer settings.
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spelling pubmed-99555672023-02-25 Selection Bias in Real-World Data Studies Used to Support Health Technology Assessments: A Case Study in Metastatic Cancer Jarada, Tamer N. O’Sullivan, Dylan E. Brenner, Darren R. Cheung, Winson Y. Boyne, Devon J. Curr Oncol Article Real-world evidence has been increasingly used to support evaluations of emerging therapies. These investigations are often conducted in settings that may not be representative of the underlying population. The purpose of this investigation was to empirically quantify the magnitude of this selection bias. Individuals diagnosed with solid metastatic cancer in Alberta, Canada, between 2010–2019 were identified using the provincial cancer registry for 13 common metastatic sites. Two outcomes used to support oncology reimbursement decisions were examined: the proportion of individuals who initiated systemic therapy and median overall survival (OS). These outcomes were assessed in the entire population and in a subset of individuals who were referred to a medical oncologist. Among the 23,152 individuals in the entire population, 40.8% (95% CI: 40.2–41.4) initiated systemic therapy, and the median OS from diagnosis was 5.4 months (95% CI: 5.3–5.6). Among those who were referred to a medical oncologist (n = 13,372; 57.8%), 67.4% (95% CI: 66.6–68.2) initiated systemic therapy, and the median OS from diagnosis was 11.2 months (95% CI: 10.9–11.5). The magnitude of bias varied by cancer site where lower referral rates were associated with greater bias. Non-referral is an important source of selection bias in real-world investigations. Studies that rely on limited-catchment real-world data should be interpreted with caution, particularly in metastatic cancer settings. MDPI 2023-02-06 /pmc/articles/PMC9955567/ /pubmed/36826112 http://dx.doi.org/10.3390/curroncol30020151 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jarada, Tamer N.
O’Sullivan, Dylan E.
Brenner, Darren R.
Cheung, Winson Y.
Boyne, Devon J.
Selection Bias in Real-World Data Studies Used to Support Health Technology Assessments: A Case Study in Metastatic Cancer
title Selection Bias in Real-World Data Studies Used to Support Health Technology Assessments: A Case Study in Metastatic Cancer
title_full Selection Bias in Real-World Data Studies Used to Support Health Technology Assessments: A Case Study in Metastatic Cancer
title_fullStr Selection Bias in Real-World Data Studies Used to Support Health Technology Assessments: A Case Study in Metastatic Cancer
title_full_unstemmed Selection Bias in Real-World Data Studies Used to Support Health Technology Assessments: A Case Study in Metastatic Cancer
title_short Selection Bias in Real-World Data Studies Used to Support Health Technology Assessments: A Case Study in Metastatic Cancer
title_sort selection bias in real-world data studies used to support health technology assessments: a case study in metastatic cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955567/
https://www.ncbi.nlm.nih.gov/pubmed/36826112
http://dx.doi.org/10.3390/curroncol30020151
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