{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T08:57:26Z","timestamp":1790758646716,"version":"4.1.0"},"reference-count":22,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82304973"],"award-info":[{"award-number":["82304973"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023TQ0018"],"award-info":[{"award-number":["2023TQ0018"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Gen. Psychiatry"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Estimating clinically important differences (CIDs) for patient\u2010reported outcome measures (PROMs) is essential for translating statistical findings into clinically meaningful treatment effects. We developed estimating clinically important differences (ESTICID), a cross\u2010platform toolkit implemented in both R Shiny and statistical analysis system, to estimate and visualise CIDs in longitudinal studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>ESTICID applies an anchor\u2010based approach using linear mixed\u2010effects models with random intercepts. The framework accommodates repeated measures, supports both continuous and categorical anchors and handles incomplete follow\u2010up data under a missing\u2010at\u2010random assumption. A multicenter randomised controlled trial dataset was used for illustration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Examples<\/jats:title>\n                    <jats:p>Using the illustrative dataset, we show that ESTICID generates clinically interpretable CID estimates across different levels of improvement and provides graphical outputs that facilitate interpretation. In simulation analyses, the estimates remained stable under increasing levels of missing follow\u2010up data. The R Shiny implementation further enables interactive, browser\u2010based use without programming.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ESTICID provides a practical and accessible framework for anchor\u2010based CID estimation in longitudinal PROM research. By integrating methodological rigour with cross\u2010platform implementation, it may improve the interpretation of treatment effects, responder definitions, sample size estimation and evidence\u2010to\u2010decision processes in psychiatry and related clinical fields.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/gps3.70041","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T03:03:59Z","timestamp":1786676639000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing clinical interpretation of patient\u2010reported outcomes: A cross\u2010platform toolkit for clinically important difference estimation using R Shiny and statistical analysis system"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5888-461X","authenticated-orcid":false,"given":"Zongshi","family":"Qin","sequence":"first","affiliation":[{"name":"Center for Digital Health and Artificial Intelligence Peking University First Hospital  Beijing China"},{"name":"Clinical Research Institute Institute of Advanced Clinical Medicine Peking University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3278-0810","authenticated-orcid":false,"given":"Yongpei","family":"Yu","sequence":"additional","affiliation":[{"name":"Clinical Research Institute Institute of Advanced Clinical Medicine Peking University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8840-0599","authenticated-orcid":false,"given":"Kuan","family":"Liao","sequence":"additional","affiliation":[{"name":"JC STEM Lab of Digital Oncology Care Enhancement (DOCE) The Hong Kong Jockey Club Charities Trust  Hong Kong 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