{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:46:06Z","timestamp":1792601166503,"version":"4.5.0"},"reference-count":50,"publisher":"Wiley","issue":"23-24","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Discrete Choice Experiments (DCEs) investigate participants' preferences by observing their choice behavior in hypothetical scenarios and are widely used in the domain of healthcare. To reduce participants' cognitive burden, especially when dealing with a large number of attributes, researchers often employ partial profile designs. In these designs, certain attributes within each choice set are kept constant. Current literature on partial profile designs mainly focuses on main\u2010effects models rather than interaction\u2010effect models, with certain partial profile designs even incapable of estimating interaction effects. To address this issue, this paper introduces a Simulated Annealing (SA) algorithm to construct partial profile designs based on an interaction\u2010effects model. During the experimental design phase, the existence and magnitude of interaction effects are often unknown. Therefore, this paper proposes a model\u2010robust experimental design strategy. Through extensive simulation experiments and a real\u2010life case study, we demonstrate that our SA model\u2010robust partial profile design performs relatively well regardless of the underlying model.<\/jats:p>","DOI":"10.1002\/sim.70770","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T03:25:08Z","timestamp":1791948308000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Simulated Annealing for Model\u2010Robust Partial Profile Choice Designs in Healthcare Preference Studies"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4554-5161","authenticated-orcid":false,"given":"Yicheng","family":"Mao","sequence":"first","affiliation":[{"name":"Department of Data Analytics and Digitalization Maastricht University  Maastricht the Netherlands"},{"name":"Department of Mathematics and Statistics University of Calgary  Calgary Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roselinde","family":"Kessels","sequence":"additional","affiliation":[{"name":"Department of Data Analytics and Digitalization Maastricht University  Maastricht the Netherlands"},{"name":"Department of Economics, City Campus University of Antwerp  Antwerp Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40273-014-0170-x"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6882"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.37972.497234.44"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhealeco.2005.09.002"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/hec.1396"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1086\/208721"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.2501\/S1470785310201673"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/0233188031000154812"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jval.2012.08.2223"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jval.2017.10.019"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolecon.2022.107665"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/hec.3858"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1177\/002224379603300305"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1509\/jmkr.38.4.430.18904"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2018.04.022"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jval.2019.04.1924"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.2196\/32365"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1509\/jmkr.43.3.409"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jspi.2003.07.005"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1755-5345(13)70042-3"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.2065"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2016.08.010"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24713-5_8"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1080\/01490409009513087"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.1080\/00224065.2013.11917916"},{"key":"e_1_2_12_27_1","doi-asserted-by":"publisher","DOI":"10.1080\/15598608.2017.1299055"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jval.2014.11.008"},{"key":"e_1_2_12_29_1","first-page":"299","article-title":"The Cost of Simplifying Preference Models","volume":"5","author":"Hagerty M. 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