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Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis

Chinyereugo M. Umemneku-Chikere Orcid Logo, Lorna Wheaton Orcid Logo, Heather Poad Orcid Logo, Devleena Ray, Ilse Cuevas Andrade, Sam Khan, Paul Tappenden, Keith R. Abrams, Rhiannon Owen Orcid Logo, Sylwia Bujkiewicz Orcid Logo

Journal of Clinical Epidemiology, Volume: 164, Pages: 96 - 103

Swansea University Author: Rhiannon Owen Orcid Logo

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DOI (Published version): 10.1016/j.jclinepi.2023.10.018

Abstract

Objectives: We aimed to develop a network meta-analytic model for the evaluation of treatment effectiveness within predictive biomarker subgroups, by combining evidence from individual participant data (IPD) from digital sources (in the absence of randomized controlled trials) and aggregate data (AD...

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Published in: Journal of Clinical Epidemiology
ISSN: 0895-4356
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa65015
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Study Design and Setting: A Bayesian framework was developed for modeling time-to-event data to evaluate predictive biomarkers. IPD were sourced from electronic health records, using a target trial emulation approach, or digitized Kaplan-Meier curves. The model is illustrated using two examples: breast cancer with a hormone receptor biomarker, and metastatic colorectal cancer with the Kirsten Rat Sarcoma (KRAS) biomarker. Results: The model allowed for the estimation of treatment effects in two subgroups of patients defined by their biomarker status. Effectiveness of taxanes did not differ in hormone receptor positive and negative breast cancer patients. Epidermal growth factor receptor inhibitors were more effective than chemotherapy in KRAS wild type colorectal cancer patients but not in patients with KRAS mutant status. Use of IPD reduced uncertainty of the subgroup-specific treatment effect estimates by up to 49%. 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spelling v2 65015 2023-11-20 Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis 0d30aa00eef6528f763a1e1589f703ec 0000-0001-5977-376X Rhiannon Owen Rhiannon Owen true false 2023-11-20 HDAT Objectives: We aimed to develop a network meta-analytic model for the evaluation of treatment effectiveness within predictive biomarker subgroups, by combining evidence from individual participant data (IPD) from digital sources (in the absence of randomized controlled trials) and aggregate data (AD). Study Design and Setting: A Bayesian framework was developed for modeling time-to-event data to evaluate predictive biomarkers. IPD were sourced from electronic health records, using a target trial emulation approach, or digitized Kaplan-Meier curves. The model is illustrated using two examples: breast cancer with a hormone receptor biomarker, and metastatic colorectal cancer with the Kirsten Rat Sarcoma (KRAS) biomarker. Results: The model allowed for the estimation of treatment effects in two subgroups of patients defined by their biomarker status. Effectiveness of taxanes did not differ in hormone receptor positive and negative breast cancer patients. Epidermal growth factor receptor inhibitors were more effective than chemotherapy in KRAS wild type colorectal cancer patients but not in patients with KRAS mutant status. Use of IPD reduced uncertainty of the subgroup-specific treatment effect estimates by up to 49%. Conclusion: Utilization of IPD allowed for more detailed evaluation of predictive biomarkers and cancer therapies and improved precision of the estimates compared to use of AD alone. Journal Article Journal of Clinical Epidemiology 164 96 103 Elsevier BV 0895-4356 IPD network meta-analysis, Network meta-regression, Predictive biomarker, Colorectal cancer, Breast cancer, One-stage Bayesian hierarchical model 31 12 2023 2023-12-31 10.1016/j.jclinepi.2023.10.018 http://dx.doi.org/10.1016/j.jclinepi.2023.10.018 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University This research was funded by the Medical Research Council, Methodology Research Panel (grant no. MR/T025166/1) and partly supported by Health Data Research UK, an initiative funded by UK Research and Innovation, Department of Health, and Social Care (England) and the devolved administrations, and leading medical research charities. 2023-12-13T12:14:05.4196317 2023-11-20T10:57:52.0455303 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Chinyereugo M. Umemneku-Chikere 0000-0003-4114-2227 1 Lorna Wheaton 0000-0002-2318-7109 2 Heather Poad 0000-0001-5058-4292 3 Devleena Ray 4 Ilse Cuevas Andrade 5 Sam Khan 6 Paul Tappenden 7 Keith R. Abrams 8 Rhiannon Owen 0000-0001-5977-376X 9 Sylwia Bujkiewicz 0000-0002-3003-9403 10 65015__29256__741965ba42164c708d8b6aec9d11c7d8.pdf 65015.VOR.pdf 2023-12-13T12:12:12.0252999 Output 947822 application/pdf Version of Record true © 2023 The Author(s). Published by Elsevier Inc. Distributed under the terms of a Creative Commons Attribution 4.0 International License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
spellingShingle Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
Rhiannon Owen
title_short Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
title_full Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
title_fullStr Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
title_full_unstemmed Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
title_sort Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
author_id_str_mv 0d30aa00eef6528f763a1e1589f703ec
author_id_fullname_str_mv 0d30aa00eef6528f763a1e1589f703ec_***_Rhiannon Owen
author Rhiannon Owen
author2 Chinyereugo M. Umemneku-Chikere
Lorna Wheaton
Heather Poad
Devleena Ray
Ilse Cuevas Andrade
Sam Khan
Paul Tappenden
Keith R. Abrams
Rhiannon Owen
Sylwia Bujkiewicz
format Journal article
container_title Journal of Clinical Epidemiology
container_volume 164
container_start_page 96
publishDate 2023
institution Swansea University
issn 0895-4356
doi_str_mv 10.1016/j.jclinepi.2023.10.018
publisher Elsevier BV
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
url http://dx.doi.org/10.1016/j.jclinepi.2023.10.018
document_store_str 1
active_str 0
description Objectives: We aimed to develop a network meta-analytic model for the evaluation of treatment effectiveness within predictive biomarker subgroups, by combining evidence from individual participant data (IPD) from digital sources (in the absence of randomized controlled trials) and aggregate data (AD). Study Design and Setting: A Bayesian framework was developed for modeling time-to-event data to evaluate predictive biomarkers. IPD were sourced from electronic health records, using a target trial emulation approach, or digitized Kaplan-Meier curves. The model is illustrated using two examples: breast cancer with a hormone receptor biomarker, and metastatic colorectal cancer with the Kirsten Rat Sarcoma (KRAS) biomarker. Results: The model allowed for the estimation of treatment effects in two subgroups of patients defined by their biomarker status. Effectiveness of taxanes did not differ in hormone receptor positive and negative breast cancer patients. Epidermal growth factor receptor inhibitors were more effective than chemotherapy in KRAS wild type colorectal cancer patients but not in patients with KRAS mutant status. Use of IPD reduced uncertainty of the subgroup-specific treatment effect estimates by up to 49%. Conclusion: Utilization of IPD allowed for more detailed evaluation of predictive biomarkers and cancer therapies and improved precision of the estimates compared to use of AD alone.
published_date 2023-12-31T12:14:06Z
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