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http://hdl.handle.net/1942/34392
Title: | From Population to Subject-Specific Reference Intervals | Authors: | PUSPARUM, Murih Ertaylan, Gökhan THAS, Olivier |
Issue Date: | 2020 | Publisher: | Springer International Publishing | Source: | Krzhizhanovskaya, Valeria V.; Závodszky, Gábor; Lees, Michael H.; Dongarra, Jack J.; Sloot, Peter M. A.; Brissos, Sérgio; Teixeira, João (Ed.). Computational Science- ICCS 2020, PT IV, Springer International Publishing, p. 468 -482 | Series/Report: | Lecture Notes in Computer Science | Series/Report no.: | 12140 | Abstract: | In clinical practice, normal values or reference intervals are the main point of reference for interpreting a wide array of measurements , including biochemical laboratory tests, anthropometrical measurements , physiological or physical ability tests. They are historically defined to separate a healthy population from unhealthy and therefore serve a diagnostic purpose. Numerous cross-sectional studies use various classical parametric and nonparametric approaches to calculate reference intervals. Based on a large cross-sectional study (N = 60,799), we compute reference intervals for subpopulations (e.g. males and females) which illustrate that subpopulations may have their own specific and more narrow reference intervals. We further argue that each healthy subject may actually have its own reference interval (subject-specific reference intervals or SSRIs). However, for estimating such SSRIs longitudinal data are required, for which the traditional reference interval estimating methods cannot be used. In this study, a linear quantile mixed model (LQMM) is proposed for estimating SSRIs from longitudinal data. The SSRIs can help clinicians to give a more accurate diagnosis as they provide an interval for each individual patient. We conclude that it is worthwhile to develop a dedicated methodology to bring the idea of subject-specific reference intervals to the preventive healthcare landscape. | Keywords: | Clinical statistics;Clinical biochemistry;Reference intervals;Longitudinal data;Quantile mixed models | Document URI: | http://hdl.handle.net/1942/34392 | ISBN: | 9783030504229 9783030504236 |
DOI: | 10.1007/978-3-030-50423-6_35 | ISI #: | 000841766600035 | Rights: | Springer Nature Switzerland AG 2020. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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Pusparum2020_Chapter_FromPopulationToSubject-Specif.pdf Restricted Access | Published version | 2.99 MB | Adobe PDF | View/Open Request a copy |
Pusparum_etal_From-Population-to-Subject-Specific_ICCS_2020.pdf | Peer-reviewed author version | 1.35 MB | Adobe PDF | View/Open |
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