Astrid Kramer, Lucas F van Schaik, Daan van den Broek, Gerrit A Meijer, Iñaki Gutierrez Ibarluzea, Lorea Galnares Cordero, Remond J A Fijneman, Marjolijn J L Ligtenberg, Ed Schuuring, Wim H van Harten, Veerle M H Coupé, Valesca P Retèl
{"title":"Towards Recommendations for Cost-Effectiveness Analysis of Predictive, Prognostic, and Serial Biomarker Tests in Oncology.","authors":"Astrid Kramer, Lucas F van Schaik, Daan van den Broek, Gerrit A Meijer, Iñaki Gutierrez Ibarluzea, Lorea Galnares Cordero, Remond J A Fijneman, Marjolijn J L Ligtenberg, Ed Schuuring, Wim H van Harten, Veerle M H Coupé, Valesca P Retèl","doi":"10.1007/s40273-025-01470-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Cost-effectiveness analysis (CEA) of biomarkers is challenging due to the indirect impact on health outcomes and the lack of sufficient fit-for-purpose data. Hands-on guidance is lacking.</p><p><strong>Objective: </strong>We aimed firstly to explore how CEAs in the context of three different types of biomarker applications have addressed these challenges, and secondly to develop recommendations for future CEAs.</p><p><strong>Methods: </strong>A scoping review was performed for three biomarker applications: predictive, prognostic, and serial testing, in advanced non-small cell lung cancer, early-stage colorectal cancer, and all-stage colorectal cancer, respectively. Information was extracted on the model assumptions and uncertainty, and the reported outcomes. An in-depth analysis of the literature was performed describing the impact of model assumptions in the included studies.</p><p><strong>Results: </strong>A total of 43 CEAs were included (31 predictive, 6 prognostic, and 6 serial testing). Of these, 40 utilized different sources for test and treatment parameters, and three studies utilized a single source. Test performance was included in 78% of these studies utilizing different sources, but this parameter was differently expressed across biomarker applications. Sensitivity analyses for test performance was only performed in half of these studies. For the linkage of test results to treatments outcomes, a minority of the studies explored the impact of suboptimal adherence to test results, and/or explored potential differences in treatment effects for different biomarker subgroups. Intermediate outcomes were reported by 67% of studies.</p><p><strong>Conclusions: </strong>We identified various approaches for dealing with challenges in CEAs of biomarker tests for three different biomarker applications. Recommendations on assumptions, handling uncertainty, and reported outcomes were drafted to enhance modeling practices for future biomarker cost-effectiveness evaluations.</p>","PeriodicalId":19807,"journal":{"name":"PharmacoEconomics","volume":" ","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PharmacoEconomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s40273-025-01470-7","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Cost-effectiveness analysis (CEA) of biomarkers is challenging due to the indirect impact on health outcomes and the lack of sufficient fit-for-purpose data. Hands-on guidance is lacking.
Objective: We aimed firstly to explore how CEAs in the context of three different types of biomarker applications have addressed these challenges, and secondly to develop recommendations for future CEAs.
Methods: A scoping review was performed for three biomarker applications: predictive, prognostic, and serial testing, in advanced non-small cell lung cancer, early-stage colorectal cancer, and all-stage colorectal cancer, respectively. Information was extracted on the model assumptions and uncertainty, and the reported outcomes. An in-depth analysis of the literature was performed describing the impact of model assumptions in the included studies.
Results: A total of 43 CEAs were included (31 predictive, 6 prognostic, and 6 serial testing). Of these, 40 utilized different sources for test and treatment parameters, and three studies utilized a single source. Test performance was included in 78% of these studies utilizing different sources, but this parameter was differently expressed across biomarker applications. Sensitivity analyses for test performance was only performed in half of these studies. For the linkage of test results to treatments outcomes, a minority of the studies explored the impact of suboptimal adherence to test results, and/or explored potential differences in treatment effects for different biomarker subgroups. Intermediate outcomes were reported by 67% of studies.
Conclusions: We identified various approaches for dealing with challenges in CEAs of biomarker tests for three different biomarker applications. Recommendations on assumptions, handling uncertainty, and reported outcomes were drafted to enhance modeling practices for future biomarker cost-effectiveness evaluations.
期刊介绍：
PharmacoEconomics is the benchmark journal for peer-reviewed, authoritative and practical articles on the application of pharmacoeconomics and quality-of-life assessment to optimum drug therapy and health outcomes. An invaluable source of applied pharmacoeconomic original research and educational material for the healthcare decision maker.
PharmacoEconomics is dedicated to the clear communication of complex pharmacoeconomic issues related to patient care and drug utilization.
PharmacoEconomics offers a range of additional features designed to increase the visibility, readership and educational value of the journal’s content. Each article is accompanied by a Key Points summary, giving a time-efficient overview of the content to a wide readership. Articles may be accompanied by plain language summaries to assist readers who have some knowledge of, but not in-depth expertise in, the area to understand the scientific content and overall implications of the article.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
