Estimating Growth in Height from Limited Longitudinal Growth Data Using Full-Curves Training Dataset: A Comparison of Two Procedures of Curve Optimization-Functional Principal Component Analysis and SITAR.
A variety of models are available for the estimation of parameters of the human growth curve. Several have been widely and successfully used with longitudinal data that are reasonably complete. On the other hand, the modeling of data for a limited number of observation points is problematic and requ...
| Publicado en: | Children Vol. 8; no. 10; pp. 1 - 22 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
MDPI
Oct2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153254921&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153254921 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22279067 LW6K jtl: Children issn: 22279067 maglogo: N pubinfo: dt: Oct2021 vid: 8 iid: 10 pid: 97109 pub: MDPI artinfo: ui: 153254921 10.3390/children8100934 153254921 ppf: 1 ppct: 21 formats: tig: atl: Estimating Growth in Height from Limited Longitudinal Growth Data Using Full-Curves Training Dataset: A Comparison of Two Procedures of Curve Optimization-Functional Principal Component Analysis and SITAR. aug: au: Králík, Miroslav Klíma, Ondrej Cuta, Martin Malina, Robert M. Kozieł, Sławomir Polcerová, Lenka Škultétyová, Anna Španel, Michal Kukla, Lubomír Zemcík, Pavel affil: Department of Anthropology, Faculty of Science, Masaryk University, 611 37 Brno, Czech Republic. sug: ab: A variety of models are available for the estimation of parameters of the human growth curve. Several have been widely and successfully used with longitudinal data that are reasonably complete. On the other hand, the modeling of data for a limited number of observation points is problematic and requires the interpolation of the interval between points and often an extrapolation of the growth trajectory beyond the range of empirical limits (prediction). This study tested a new approach for fitting a relatively limited number of longitudinal data using the normal variation of human empirical growth curves. First, functional principal components analysis was done for curve phase and amplitude using complete and dense data sets for a reference sample (Brno Growth Study). Subsequently, artificial curves were generated with a combination of 12 of the principal components and applied for fitting to the newly analyzed data with the Levenberg-Marquardt optimization algorithm. The approach was tested on seven 5-points/year longitudinal data samples of adolescents extracted from the reference sample. The samples differed in their distance from the mean age at peak velocity for the sample and were tested by a permutation leave-one-out approach. The results indicated the potential of this method for growth modeling as a user-friendly application for practical applications in pediatrics, auxology and youth sport. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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