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Modelling height in adolescence: a comparison of methods for estimating the age at peak height velocity

Background: Controlling for maturational status and timing is crucial in lifecourse epidemiology. One popular non-invasive measure of maturity is the age at peak height velocity (PHV). There are several ways to estimate age at PHV, but it is unclear which of these to use in practice. Aim: To find th...

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Autores principales: Simpkin, Andrew J., Sayers, Adrian, Gilthorpe, Mark S., Heron, Jon, Tilling, Kate
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5743008/
https://www.ncbi.nlm.nih.gov/pubmed/29113497
http://dx.doi.org/10.1080/03014460.2017.1391877
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author Simpkin, Andrew J.
Sayers, Adrian
Gilthorpe, Mark S.
Heron, Jon
Tilling, Kate
author_facet Simpkin, Andrew J.
Sayers, Adrian
Gilthorpe, Mark S.
Heron, Jon
Tilling, Kate
author_sort Simpkin, Andrew J.
collection PubMed
description Background: Controlling for maturational status and timing is crucial in lifecourse epidemiology. One popular non-invasive measure of maturity is the age at peak height velocity (PHV). There are several ways to estimate age at PHV, but it is unclear which of these to use in practice. Aim: To find the optimal approach for estimating age at PHV. Subjects and methods: Methods included the Preece & Baines non-linear growth model, multi-level models with fractional polynomials, SuperImposition by Translation And Rotation (SITAR) and functional data analysis. These were compared through a simulation study and using data from a large cohort of adolescent boys from the Christ’s Hospital School. Results: The SITAR model gave close to unbiased estimates of age at PHV, but convergence issues arose when measurement error was large. Preece & Baines achieved close to unbiased estimates, but shares similarity with the data generation model for our simulation study and was also computationally inefficient, taking 24 hours to fit the data from Christ’s Hospital School. Functional data analysis consistently converged, but had higher mean bias than SITAR. Almost all methods demonstrated strong correlations (r > 0.9) between true and estimated age at PHV. Conclusions: Both SITAR or the PBGM are useful models for adolescent growth and provide unbiased estimates of age at peak height velocity. Care should be taken as substantial bias and variance can occur with large measurement error.
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spelling pubmed-57430082018-01-05 Modelling height in adolescence: a comparison of methods for estimating the age at peak height velocity Simpkin, Andrew J. Sayers, Adrian Gilthorpe, Mark S. Heron, Jon Tilling, Kate Ann Hum Biol Research Paper Background: Controlling for maturational status and timing is crucial in lifecourse epidemiology. One popular non-invasive measure of maturity is the age at peak height velocity (PHV). There are several ways to estimate age at PHV, but it is unclear which of these to use in practice. Aim: To find the optimal approach for estimating age at PHV. Subjects and methods: Methods included the Preece & Baines non-linear growth model, multi-level models with fractional polynomials, SuperImposition by Translation And Rotation (SITAR) and functional data analysis. These were compared through a simulation study and using data from a large cohort of adolescent boys from the Christ’s Hospital School. Results: The SITAR model gave close to unbiased estimates of age at PHV, but convergence issues arose when measurement error was large. Preece & Baines achieved close to unbiased estimates, but shares similarity with the data generation model for our simulation study and was also computationally inefficient, taking 24 hours to fit the data from Christ’s Hospital School. Functional data analysis consistently converged, but had higher mean bias than SITAR. Almost all methods demonstrated strong correlations (r > 0.9) between true and estimated age at PHV. Conclusions: Both SITAR or the PBGM are useful models for adolescent growth and provide unbiased estimates of age at peak height velocity. Care should be taken as substantial bias and variance can occur with large measurement error. Taylor & Francis 2017-11-07 /pmc/articles/PMC5743008/ /pubmed/29113497 http://dx.doi.org/10.1080/03014460.2017.1391877 Text en © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group http://creativecommons.org/Licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/Licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Paper
Simpkin, Andrew J.
Sayers, Adrian
Gilthorpe, Mark S.
Heron, Jon
Tilling, Kate
Modelling height in adolescence: a comparison of methods for estimating the age at peak height velocity
title Modelling height in adolescence: a comparison of methods for estimating the age at peak height velocity
title_full Modelling height in adolescence: a comparison of methods for estimating the age at peak height velocity
title_fullStr Modelling height in adolescence: a comparison of methods for estimating the age at peak height velocity
title_full_unstemmed Modelling height in adolescence: a comparison of methods for estimating the age at peak height velocity
title_short Modelling height in adolescence: a comparison of methods for estimating the age at peak height velocity
title_sort modelling height in adolescence: a comparison of methods for estimating the age at peak height velocity
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5743008/
https://www.ncbi.nlm.nih.gov/pubmed/29113497
http://dx.doi.org/10.1080/03014460.2017.1391877
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