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...
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2021-10-01
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| author | Miroslav Králík Ondřej Klíma Martin Čuta Robert M. Malina Sławomir Kozieł Lenka Polcerová Anna Škultétyová Michal Španěl Lubomír Kukla Pavel Zemčík |
| author_facet | Miroslav Králík Ondřej Klíma Martin Čuta Robert M. Malina Sławomir Kozieł Lenka Polcerová Anna Škultétyová Michal Španěl Lubomír Kukla Pavel Zemčík |
| author_sort | Miroslav Králík |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:38:24Z |
| format | Article |
| id | doaj.art-460dfe39deb541a0a248f27ce326e905 |
| institution | Directory Open Access Journal |
| issn | 2227-9067 |
| language | English |
| last_indexed | 2024-03-10T06:38:24Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Children |
| spelling | doaj.art-460dfe39deb541a0a248f27ce326e9052023-11-22T17:51:28ZengMDPI AGChildren2227-90672021-10-0181093410.3390/children8100934Estimating 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 SITARMiroslav Králík0Ondřej Klíma1Martin Čuta2Robert M. Malina3Sławomir Kozieł4Lenka Polcerová5Anna Škultétyová6Michal Španěl7Lubomír Kukla8Pavel Zemčík9Department of Anthropology, Faculty of Science, Masaryk University, 611 37 Brno, Czech RepublicIT4Innovations Centre of Excellence, Brno University of Technology, 612 00 Brno, Czech RepublicDepartment of Anthropology, Faculty of Science, Masaryk University, 611 37 Brno, Czech RepublicDepartment of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX 78712-1415, USADepartment of Anthropology, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wrocław, PolandDepartment of Anthropology, Faculty of Science, Masaryk University, 611 37 Brno, Czech RepublicDepartment of Anthropology, Faculty of Science, Masaryk University, 611 37 Brno, Czech RepublicIT4Innovations Centre of Excellence, Brno University of Technology, 612 00 Brno, Czech RepublicOutpatient Primary Care Pediatric Center, 625 00 Brno, Czech RepublicIT4Innovations Centre of Excellence, Brno University of Technology, 612 00 Brno, Czech RepublicA 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.https://www.mdpi.com/2227-9067/8/10/934human growthgrowth modellingfunctional data analysisSitar |
| spellingShingle | Miroslav Králík Ondřej Klíma Martin Čuta Robert M. Malina Sławomir Kozieł Lenka Polcerová Anna Škultétyová Michal Španěl Lubomír Kukla Pavel Zemčík 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 Children human growth growth modelling functional data analysis Sitar |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | human growth growth modelling functional data analysis Sitar |
| url | https://www.mdpi.com/2227-9067/8/10/934 |
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