Early stature prediction method using stature growth parameters
- Authors
- Lee, Shin-Jae; An, Hongseok; Ahn, Sug-Joon; Kim, Young Ho; Pak, Sunyoung; Lee, Jae Won
- Issue Date
- 2008
- Publisher
- TAYLOR & FRANCIS LTD
- Keywords
- early prediction; biological parameters; multiple regressions; curve fitting
- Citation
- ANNALS OF HUMAN BIOLOGY, v.35, no.5, pp.509 - 517
- Indexed
- SCIE
SCOPUS
- Journal Title
- ANNALS OF HUMAN BIOLOGY
- Volume
- 35
- Number
- 5
- Start Page
- 509
- End Page
- 517
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/125512
- DOI
- 10.1080/03014460802286942
- ISSN
- 0301-4460
- Abstract
- Background: The creation of an accurate growth prediction method for human stature at a stage of growth has been an interesting challenge in medical science and human biology. Aim: The aim of this study was to develop a non-radiographic final stature prediction method that is applicable in the early pubertal growth period. Subjects and methods: Randomly selected 12-year serial stature growth data for 400 Koreans were fitted with two nonlinear growth curves: Preece and Baines model 1 (PB1) and Jolicoeur-Pontier-Pernin-Sempe (JPPS) functions. Five biological parameters, including take-off (TO) related parameters, were derived by differentiation of the two curves, respectively. Those five variables were composed into a multiple linear regression equation for final stature prediction. In the cross-validation subjects, TO-related variables were estimated by linear interpolation from the partial growth data prior to estimation age, then incorporated into the prediction equation. Results: The final stature prediction model had excellent validity and accuracy when applied to the cross-validation samples. Prediction accuracy increased according to increasing years after take-off. Conclusions: This study suggests that a final stature prediction method using multiple regression analysis that includes biological parameters can predict stature growth with sufficient validity and accuracy. Incorporation of TO-related parameters allowed us to develop earlier growth evaluation and prediction methods compared with other previous methods.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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