{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T18:31:48Z","timestamp":1790793108253,"version":"4.1.0"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Qual Life Res"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Recently developed minimal important change (MIC) estimation methods recover the mean individual MIC in a sample. These methods are the adjusted predictive modeling (APM) method and the longitudinal confirmatory factor analysis (LCFA) method. Both methods require LCFA of patient-reported outcome measure (PROM) data. In the APM-method, LCFA is used to estimate the reliability of the transition ratings, whereas in the LCFA-method, LCFA is used to estimate the latent MIC. However, LCFA cannot be performed if the PROM is a single item measure (SIM). Adding an auxiliary variable, that is correlated with the PROM, to the LCFA-model may be a solution. We developed three different LCFA-models in which an auxiliary variable is included. In this simulation study, we assessed the performance of the APM- and LCFA-methods to recover the true MIC of an SIM. We applied both methods to a real dataset in which the SIM was a numeric rating scale for pain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We simulated 15,552 samples, varying in 11 parameters, and estimated the APM-based and LCFA-based MICs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The APM-method performed well, except if the proportion improved was high or low, and the present state bias (PSB) was high. The LCFA-method performed well, irrespective of the proportion improved and the PSB. In the real data, the LCFA-based MIC was 17 (on a 100-point scale), whereas the estimated APM-based MIC was 4 points higher, probably due to a high proportion improved and a high PSB.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The MIC of an SIM can be accurately estimated using an auxiliary PROM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11136-025-04134-3","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T09:04:12Z","timestamp":1767949452000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimating the minimal important change of single-item measures using the adjusted predictive modeling method or the longitudinal confirmatory factor analysis method"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8944-5238","authenticated-orcid":false,"given":"Berend","family":"Terluin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong Hao","family":"Pua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piper","family":"Fromy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Trigg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babette","family":"van der Zwaard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob B.","family":"Bjorner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"issue":"10","key":"4134_CR1","doi-asserted-by":"publisher","first-page":"2729","DOI":"10.1007\/s11136-021-02925-y","volume":"30","author":"CB Terwee","year":"2021","unstructured":"Terwee, C. B., Peipert, J. D., Chapman, R., Lai, J. S., Terluin, B., Cella, D., Griffith, P., & Mokkink, L. B. (2021). Minimal important change (MIC): A conceptual clarification and systematic review of MIC estimates of PROMIS measures. Quality of Life Research, 30(10), 2729\u20132754.","journal-title":"Quality of Life Research"},{"key":"4134_CR2","unstructured":"FDA. (2019). Incorporating clinical outcome assessments into endpoints for regulatory decision-making. US Food and Drug Administration."},{"key":"4134_CR3","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1586\/erp.11.9","volume":"11","author":"MT King","year":"2011","unstructured":"King, M. T. (2011). A point of minimal important difference (MID): A critique of terminology and methods. Expert Review of Pharmacoeconomics & Outcomes Research, 11, 171\u2013184.","journal-title":"Expert Review of Pharmacoeconomics &amp; Outcomes Research"},{"issue":"1","key":"4134_CR4","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1186\/s12874-021-01307-9","volume":"21","author":"A Vanier","year":"2021","unstructured":"Vanier, A., Sebille, V., Blanchin, M., & Hardouin, J. B. (2021). The minimal perceived change: A formal model of the responder definition according to the patient\u2019s meaning of change for patient-reported outcome data analysis and interpretation. BMC Medical Research Methodology, 21(1), 128.","journal-title":"BMC Medical Research Methodology"},{"key":"4134_CR5","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.jclinepi.2016.12.015","volume":"83","author":"B Terluin","year":"2017","unstructured":"Terluin, B., Eekhout, I., & Terwee, C. B. (2017). The anchor-based minimal important change, based on receiver operating characteristic analysis or predictive modeling, May need to be adjusted for the proportion of improved patients. Journal of Clinical Epidemiology, 83, 90\u2013100.","journal-title":"Journal of Clinical Epidemiology"},{"key":"4134_CR6","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.jclinepi.2022.04.018","volume":"148","author":"B Terluin","year":"2022","unstructured":"Terluin, B., Eekhout, I., & Terwee, C. B. (2022). Improved adjusted minimal important change took reliability of transition ratings into account. Journal of Clinical Epidemiology, 148, 48\u201353.","journal-title":"Journal of Clinical Epidemiology"},{"issue":"5","key":"4134_CR7","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1007\/s11136-022-03172-5","volume":"32","author":"JB Bjorner","year":"2023","unstructured":"Bjorner, J. B., Terluin, B., Trigg, A., Hu, J., Brady, K. J. S., & Griffiths, P. (2023). Establishing thresholds for meaningful within-individual change using longitudinal item response theory. Quality of Life Research, 32(5), 1267\u20131276.","journal-title":"Quality of Life Research"},{"issue":"4","key":"4134_CR8","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1007\/s11136-023-03577-w","volume":"33","author":"B Terluin","year":"2024","unstructured":"Terluin, B., Trigg, A., Fromy, P., Schuller, W., Terwee, C. B., & Bjorner, J. B. (2024). Estimating anchor-based minimal important change using longitudinal confirmatory factor analysis. Quality of Life Research, 33(4), 963\u2013973.","journal-title":"Quality of Life Research"},{"key":"4134_CR9","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1016\/j.jclinepi.2015.03.015","volume":"68","author":"B Terluin","year":"2015","unstructured":"Terluin, B., Eekhout, I., Terwee, C. B., & de Vet, H. C. W. (2015). Minimal important change (MIC) based on a predictive modeling approach was more precise than MIC based on ROC analysis. Journal of Clinical Epidemiology, 68, 1388\u20131396.","journal-title":"Journal of Clinical Epidemiology"},{"key":"4134_CR10","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.jclinepi.2021.08.029","volume":"141","author":"P Griffiths","year":"2022","unstructured":"Griffiths, P., Terluin, B., Trigg, A., Schuller, W., & Bjorner, J. B. (2022). A confirmatory factor analysis approach was found to accurately estimate the reliability of transition ratings. Journal of Clinical Epidemiology, 141, 36\u201345.","journal-title":"Journal of Clinical Epidemiology"},{"issue":"11","key":"4134_CR11","doi-asserted-by":"publisher","first-page":"2963","DOI":"10.1007\/s11136-024-03763-4","volume":"33","author":"B Terluin","year":"2024","unstructured":"Terluin, B., Fromy, P., Trigg, A., Terwee, C. B., & Bjorner, J. B. (2024). Effect of present state bias on minimal important change estimates: A simulation study. Quality of Life Research, 33(11), 2963\u20132973.","journal-title":"Quality of Life Research"},{"issue":"6","key":"4134_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v048.i06","volume":"48","author":"RP Chalmers","year":"2012","unstructured":"Chalmers, R. P. (2012). Mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1\u201329.","journal-title":"Journal of Statistical Software"},{"key":"4134_CR13","unstructured":"R Core Team. (2016). R: A Language and environment for statistical computing. R Foundation for Statistical Computing."},{"key":"4134_CR14","doi-asserted-by":"publisher","first-page":"2","DOI":"10.18637\/jss.v048.i02","volume":"48","author":"Y Rosseel","year":"2012","unstructured":"Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. 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All participating patients have signed informed consent for using the data for quality assurance and scientific purposes. The present study concerns a secondary analysis of pseudonymized data. The Medical Ethical Committee (METC) Brabant has confirmed that the Dutch Medical Research Involving Human Subjects Act (WMO) does not apply to the present study and that an official ethical approval was not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"See Ethics approval.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}],"article-number":"39"}}