Publication detail

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

KRÁLÍK, M. KLÍMA, O. ČUTA, M. MALINA, R. KOZIEL, S. POLCEROVÁ, L. ŠKULTÉTYOVÁ, A. ŠPANĚL, M. KUKLA, L. ZEMČÍK, P.

Original 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

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

human growth, growth modelling, functional data analysis, Sitar

Authors

KRÁLÍK, M.; KLÍMA, O.; ČUTA, M.; MALINA, R.; KOZIEL, S.; POLCEROVÁ, L.; ŠKULTÉTYOVÁ, A.; ŠPANĚL, M.; KUKLA, L.; ZEMČÍK, P.

Released

18. 10. 2021

ISBN

2227-9067

Periodical

Children-Basel

Year of study

8

Number

10

State

Swiss Confederation

Pages from

934

Pages to

955

Pages count

21

URL

https://www.mdpi.com/2227-9067/8/10/934

BibTex

@article{BUT175849,
  author="KRÁLÍK, M. and KLÍMA, O. and ČUTA, M. and MALINA, R. and KOZIEL, S. and POLCEROVÁ, L. and ŠKULTÉTYOVÁ, A. and ŠPANĚL, M. and KUKLA, L. and ZEMČÍK, P.",
  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",
  journal="Children-Basel",
  year="2021",
  volume="8",
  number="10",
  pages="934--955",
  doi="10.3390/children8100934",
  issn="2227-9067",
  url="https://www.mdpi.com/2227-9067/8/10/934"
}