{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T19:59:12Z","timestamp":1790884752560,"version":"4.1.0"},"reference-count":45,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000092","name":"U.S. National Library of Medicine","doi-asserted-by":"publisher","award":["T15LM013976"],"award-info":[{"award-number":["T15LM013976"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006545","name":"National Institute on Minority Health and Health Disparities","doi-asserted-by":"publisher","award":["K01MD014163"],"award-info":[{"award-number":["K01MD014163"]}],"id":[{"id":"10.13039\/100006545","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Med Decis Making"],"abstract":"<jats:sec>\n                    <jats:title>Introduction\/Purpose:<\/jats:title>\n                    <jats:p>Distributional cost-effectiveness analysis (DCEA) extends traditional cost-effectiveness analysis by explicitly incorporating concerns about health inequality. Decision models are central to health economic evaluation, yet existing guidance focuses either on hypothetical microsimulation for standard CEA or on aggregate approaches for DCEA. There is no clear guidance on how to build an individual-level microsimulation model in R specifically to support DCEA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods\/Design:<\/jats:title>\n                    <jats:p>We provide a step-by-step tutorial for developing a microsimulation model in R that is tailored to DCEA needs. Key differences from traditional CEA modelling are highlighted to ensure that distributional effects can be evaluated. We simulate 2 intervention scenarios\u20141) making a treatment available to individuals with high blood pressure and 2) making a treatment available to individuals with high blood pressure and lower income\u2014and compare both with a no-intervention baseline. Outcomes and costs are examined across income groups. The model is parameterised using individual-level data from the US National Health and Nutrition Examination Survey to illustrate the value of large secondary datasets for DCEA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>We provide reproducible R code for building population-level microsimulation models and for conducting DCEA using indirect equity weighting. Examples of outputs relevant to DCEA, presented alongside traditional CEA results, are included. The tutorial demonstrates how distributional considerations can influence decision making and how results vary under key parameter assumptions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>This tutorial builds on existing guidance by showing how to construct and parameterise an R-based microsimulation model using individual-level secondary data and how to evaluate outcomes through DCEA. The choice of evaluation approach ultimately depends on decision makers\u2019 equity principles, and the framework provided can be adapted accordingly.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/0272989x261485229","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T11:08:12Z","timestamp":1790766492000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Distributional Cost-Effectiveness Analysis Using Microsimulation in R: A Tutorial"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7772-4822","authenticated-orcid":false,"given":"Luke E.","family":"Barry","sequence":"first","affiliation":[{"name":"Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roch A.","family":"Nianogo","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA"},{"name":"California Center for Population Research, University of California, Los Angeles, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"crossref","unstructured":"Cookson R et al. 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