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Cost-effectiveness of precision diagnostic testing for precision medicine approaches against non-small-cell lung cancer: A systematic review

Research output: Contribution to journalReview articlepeer-review

Raymond Henderson, Peter Keeling, Declan French, Dave Smart, Richard Sullivan, Mark Lawler

Original languageEnglish
Pages (from-to)2672-2687
Number of pages16
JournalMolecular Oncology
Volume15
Issue number10
DOIs
Accepted/In press2021
PublishedOct 2021

Bibliographical note

Funding Information: RH is a Knowledge Transfer Programme (KTP) Associate, KTP aim to help businesses improve their competitiveness and productivity through the better use of knowledge, technology and skills held within the UK knowledge base. KTP are funded by UKRI through Innovate UK with the support of co‐funders, including the Scottish Funding Council, Welsh Government, Invest Northern Ireland, Defra and BEIS. Innovate UK manages the KTP Programme and facilitates its delivery through a range of partners including the Knowledge Transfer Network (KTN), Knowledge Bases and Businesses. ML is supported in his role as Associate Director of Health Data Research Wales Northern Ireland through a Substantive Site grant from Health Data Research UK and in his role as Scientific Director of DATA‐CAN, the UK’s Health Data Research Hub for Cancer through the Industrial Strategy Challenge Fund. Funding Information: ML reports honoraria received from Pfizer, EMD Serono, Roche and Carnall Farrar for presentations unrelated to this work. ML also reports an unrestricted educational grant from Pfizer for activity unrelated to this work. None of the other authors have any conflicts of interest to declare. Publisher Copyright: © 2021 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors

Abstract

Precision diagnostic testing (PDT) employs appropriate biomarkers to identify cancer patients that may optimally respond to precision medicine (PM) approaches, such as treatments with targeted agents and immuno-oncology drugs. To date, there are no published systematic appraisals evaluating the cost-effectiveness of PDT in non-small-cell lung cancer (NSCLC). To address this gap, we conducted Preferred Reporting Items for Systematic Reviews and Meta-Analyses searches for the years 2009–2019. Consolidated Health Economic Evaluation Reporting Standards were employed to screen, assess and extract data. Employing base costs, life years gained or quality-adjusted life years, as well as willingness-to-pay (WTP) threshold for each country, net monetary benefit was calculated to determine cost-effectiveness of each intervention. Thirty-seven studies (50%) were included for analysis; a further 37 (50%) were excluded, having failed population-, intervention-, comparator-, outcomes- and study-design criteria. Within the 37 studies included, we defined 64 scenarios. Eleven scenarios compared PDT-guided PM with non-guided therapy [epidermal growth factor receptor (EGFR), n = 5; programmed death-ligand 1 (PD-L1), n = 6]. Twenty-eight scenarios compared PDT-guided PM with chemotherapy alone (anaplastic lymphoma kinase, n = 3; EGFR, n = 17; PD-L1, n = 8). Twenty-five scenarios compared PDT-guided PM with chemotherapy alone, while varying the PDT approach. Thirty-four scenarios (53%) were cost-effective, 28 (44%) were not cost-effective, and two were marginal, dependent on their country’s WTP threshold. When PDT-guided therapy was compared with a therapy-for-all patients approach, all scenarios (100%) proved cost-effective. Seven of 37 studies had been structured appropriately to assess PDT-PM cost-effectiveness. Within these seven studies, all evaluated scenarios were cost-effective. However, 81% of studies had been poorly designed. Our systematic analysis implies that more robust health economic evaluation could help identify additional approaches towards PDT cost-effectiveness, underpinning value-based care and enhanced outcomes for patients with NSCLC.

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