{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T04:44:39Z","timestamp":1782621879138,"version":"3.54.5"},"reference-count":32,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Melanoma Focus"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["MDM Policy &amp; Practice"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    <jats:bold>Purpose.<\/jats:bold>\n                    The diagnosis, management, and therapy of cancer are rapidly advancing and becoming more costly. Data linking the diagnosis, management, and prescription of systemic anticancer therapy (SACT) are not publicly available, are time-consuming to obtain, and are onerous to analyze. We aimed to illustrate how a simple expert elicitation method can be used rapidly to calculate the cost of diagnosing and treating a cancer by stage of disease. We illustrate the method using the example of melanoma.\n                    <jats:bold>Methods.<\/jats:bold>\n                    We designed a simple structured elicitation exercise for melanoma experts from the United Kingdom with the aim of describing the diagnosis, management, and SACT prescription for people with melanoma and the associated proportions offered each option. We modeled experts\u2019 beliefs using scaled beta distributions. We used random-effects meta-analysis to combine the estimates. Published unit costs (\u00a3; 2024\u20132025) were multiplied by estimated proportions to calculate the mean costs for the diagnosis, management, and SACT by disease stage.\n                    <jats:bold>Results.<\/jats:bold>\n                    Seven dermatologists, 5 oncologists, and 4 surgeons participated (2022\u20132023). There was variation in the estimates of the proportions receiving possible diagnosis, management, or SACT options. Diagnosing suspicious lesions cost \u00a3424 to \u00a3699 depending on the investigations required. Mean (95% confidence intervals) management costs were \u00a3782 (\u00a3611, \u00a3981) for stage 0, \u00a3946 (\u00a3776, \u00a31,145) for stage 1a, \u00a34,729 (\u00a34,532, \u00a34,944) for stage 1b\/2, \u00a33,719 (\u00a33,213, \u00a34,150) for macroscopic 3, and \u00a32,827 (\u00a32,757, \u00a32,904) for microscopic 3. Mean SACT costs were \u00a365,289 (\u00a350,581, \u00a374,521) for stage 3 and \u00a3134,065 (\u00a3115,787, \u00a3152,938) for stage 4.\n                    <jats:bold>Conclusions.<\/jats:bold>\n                    Our new approach to eliciting and combining estimates balances practical and theoretical considerations. We illustrate how this method produces estimates of the costs, and associated uncertainty, of the diagnosis, management, and SACT for melanoma by stage of disease in the absence of readily accessible diagnosis, management, and prescribing data. These cost estimates were not validated against registry data.\n                  <\/jats:p>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:list list-type=\"bullet\">\n                      <jats:list-item>\n                        <jats:p>Linked diagnosis, primary surgical management (hereafter \u201cmanagement\u201d), and prescribing of systemic anticancer therapies (SACT) data describing the number and proportion of people with cancer offered these options are not readily available. The time to obtain data together with the time to conduct onerous analyses are a key barrier to obtaining values for use by decision analysts building economic models for evaluating new options for the early detection or prevention of cancer.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>We designed a simple expert elicitation exercise, completed in Excel. The exercise was designed to provide estimates of the total cost of cancer by stage of disease and crucially also to understand the extent of uncertainty around these estimates. We used melanoma as an exemplar cancer.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>Clinical experts (consultant dermatologists, surgeons, and oncologists) estimated the proportions of people offered possible diagnostic, management, or SACT options for melanoma (by stage of disease). Our study suggested that they found the expert elicitation exercise easy to understand and managed to complete the task.<\/jats:p>\n                      <\/jats:list-item>\n                      <jats:list-item>\n                        <jats:p>Random-effects meta-analysis used to pool the individual estimates indicated substantial heterogeneity across experts\u2019 estimates of most elicited proportions. This finding suggested wide variation in possible approaches to the diagnosis, management, and use of SACT for melanoma.<\/jats:p>\n                      <\/jats:list-item>\n                    <\/jats:list>\n                  <\/jats:sec>","DOI":"10.1177\/23814683261451278","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T04:40:00Z","timestamp":1782448800000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Method for Estimating Resource Use and Costs when Empirical Data Are Unavailable: Expert Elicitation Study Using the Example of Melanoma"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3475-800X","authenticated-orcid":false,"given":"Rob","family":"Hainsworth","sequence":"first","affiliation":[{"name":"Division of Population Health, Health Services Research and Primary Care, Manchester Centre for Health Economics, School of Health Sciences, The University of Manchester, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3159-4249","authenticated-orcid":false,"given":"Louisa","family":"Collins","sequence":"additional","affiliation":[{"name":"Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia"},{"name":"School of Nursing, Queensland University of Technology (QUT), Brisbane, QLD, Australia"},{"name":"School of Public Health, University of Queensland, Brisbane, QLD, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1542-2527","authenticated-orcid":false,"given":"Martin","family":"Eden","sequence":"additional","affiliation":[{"name":"Division of Population Health, Health Services Research and Primary Care, Manchester Centre for Health Economics, School of Health Sciences, The University of Manchester, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2753-4841","authenticated-orcid":false,"given":"Adele","family":"Green","sequence":"additional","affiliation":[{"name":"Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia"},{"name":"Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8875-2164","authenticated-orcid":false,"given":"Paul","family":"Lorigan","sequence":"additional","affiliation":[{"name":"The Christie NHS Foundation Trust, Manchester, UK"},{"name":"Division of Cancer Sciences, School of Medical Sciences, The University of Manchester, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9339-7374","authenticated-orcid":false,"given":"Gabriel","family":"Rogers","sequence":"additional","affiliation":[{"name":"Division of Population Health, Health Services Research and Primary Care, Manchester Centre for Health Economics, School of Health Sciences, The University of Manchester, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1557-5918","authenticated-orcid":false,"given":"Amber","family":"Salisbury","sequence":"additional","affiliation":[{"name":"Division of Population Health, Health Services Research and Primary Care, Manchester Centre for Health Economics, School of Health Sciences, The University of Manchester, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3938-4350","authenticated-orcid":false,"given":"Katherine","family":"Payne","sequence":"additional","affiliation":[{"name":"Division of Population Health, Health Services Research and Primary Care, Manchester Centre for Health Economics, School of Health Sciences, The University of Manchester, Manchester, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Screening for cancer: the economic, medical, and psychosocial issues","volume":"26","author":"Brill JV.","year":"2020","unstructured":"Brill JV. 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