From population to subject-specific reference intervals
(2020)
ICCS 2020: Computational Science.
In Lecture Notes in Computer Science
12140(Part IV).
p.468-482
- Author
- Murih Pusparum, Gökhan Ertaylan and Olivier Thas (UGent)
- Organization
- 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
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Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8745335
- MLA
- Pusparum, Murih, et al. “From Population to Subject-Specific Reference Intervals.” ICCS 2020: Computational Science, edited by Valeria V. Krzhizhanovskaya et al., vol. 12140, no. Part IV, Springer, 2020, pp. 468–82, doi:10.1007/978-3-030-50423-6_35.
- APA
- Pusparum, M., Ertaylan, G., & Thas, O. (2020). From population to subject-specific reference intervals. In V. V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, J. J. Dongarra, P. M. A. Sloot, S. Brissos, & J. Teixeira (Eds.), ICCS 2020: Computational Science (Vol. 12140, pp. 468–482). https://doi.org/10.1007/978-3-030-50423-6_35
- Chicago author-date
- Pusparum, Murih, Gökhan Ertaylan, and Olivier Thas. 2020. “From Population to Subject-Specific Reference Intervals.” In ICCS 2020: Computational Science, edited by Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, and João Teixeira, 12140:468–82. Cham: Springer. https://doi.org/10.1007/978-3-030-50423-6_35.
- Chicago author-date (all authors)
- Pusparum, Murih, Gökhan Ertaylan, and Olivier Thas. 2020. “From Population to Subject-Specific Reference Intervals.” In ICCS 2020: Computational Science, ed by. Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, and João Teixeira, 12140:468–482. Cham: Springer. doi:10.1007/978-3-030-50423-6_35.
- Vancouver
- 1.Pusparum M, Ertaylan G, Thas O. From population to subject-specific reference intervals. In: Krzhizhanovskaya VV, Závodszky G, Lees MH, Dongarra JJ, Sloot PMA, Brissos S, et al., editors. ICCS 2020: Computational Science. Cham: Springer; 2020. p. 468–82.
- IEEE
- [1]M. Pusparum, G. Ertaylan, and O. Thas, “From population to subject-specific reference intervals,” in ICCS 2020: Computational Science, Amsterdam, The Netherlands, 2020, vol. 12140, no. Part IV, pp. 468–482.
@inproceedings{8745335,
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.}},
author = {{Pusparum, Murih and Ertaylan, Gökhan and Thas, Olivier}},
booktitle = {{ICCS 2020: Computational Science}},
editor = {{Krzhizhanovskaya, Valeria V. and Závodszky, Gábor and Lees, Michael H. and Dongarra, Jack J. and Sloot, Peter M. A. and Brissos, Sérgio and Teixeira, João}},
isbn = {{9783030504229}},
issn = {{0302-9743}},
keywords = {{Clinical statistics,Clinical biochemistry,Reference intervals,Longitudinal data,Quantile mixed models}},
language = {{eng}},
location = {{Amsterdam, The Netherlands}},
number = {{Part IV}},
pages = {{468--482}},
publisher = {{Springer}},
title = {{From population to subject-specific reference intervals}},
url = {{http://doi.org/10.1007/978-3-030-50423-6_35}},
volume = {{12140}},
year = {{2020}},
}
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