{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T02:41:19Z","timestamp":1793500879528,"version":"4.5.0"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Accurate prediction of patient outcomes in clinical trials is crucial for the timely assessment of treatment efficacy. This study proposes a novel approach to predict patient response using longitudinal clinical data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We construct temporal trajectories from longitudinal data and extrapolate these trajectories to forecast individual patient outcomes. Additionally, we assess when new patients align with established response patterns. The approach is evaluated using data from the MGTX trial involving patients with myasthenia gravis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our analysis demonstrates the predictability of patient trajectories and enables automatic clustering of patients based on treatment success. The clustering reveals potential associations with age and smoking status.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings highlight the potential of trajectory-based methods for early prediction of treatment response in clinical trials. We also discuss possible confounding factors that may influence the observed associations and predictive performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1755031","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:50:34Z","timestamp":1776837034000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Construction of patient trajectories to model clinical trial outcomes: application to myasthenia gravis"],"prefix":"10.3389","volume":"8","author":[{"given":"Marc","family":"Garbey","sequence":"first","affiliation":[{"name":"Care Constitution Corp","place":["Houston, TX, United States"]},{"name":"LaSIE, UMR-CNRS 7356 University of La Rochelle","place":["La Rochelle, France"]},{"name":"Department of Neurology & Rehabilitation Medicine, George Washington University","place":["Washington, DC, United 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