Estimating Peak Height Velocity in Individuals: a Comparison of Statistical Methods

Overview

Journal Ann Hum Biol

Specialty Biology

Date 2020 Jun 17

PMID 32543236

Citations 8

Authors

Melanie E Boeyer

Kevin M Middleton

Dana L Duren

Emily V Leary

Affiliations

Soon will be listed here.

Abstract

Background: Estimates pertaining to the timing of the adolescent growth spurt (e.g. peak height velocity; PHV), including age at peak height velocity (aPHV), play a critical role in the diagnosis, treatment, and management of skeletal growth and/or developmental disorders. Yet, distinct statistical methodologies often result in large estimate discrepancies.

Aim: The aim of the present study was to assess the advantages and disadvantages of three modelling methodologies for height as well as to determine how estimates derived from these methodologies may differ, particularly those that may be useful in paediatric clinical practice.

Subjects And Methods: Height data from 686 individuals of the Fels Longitudinal Study were modelled using 5th order polynomials, natural cubic splines, and SuperImposition by Translation and Rotation (SITAR) to determine aPHV and PHV for all individuals together (i.e. population average) by sex and separately for each individual. Estimates within and between methodologies were calculated and compared.

Results: In general, mean aPHV was earlier, and PHV was greater for individuals when compared to estimates from population average models. Significant differences between mean aPHV and PHV for individuals were observed in all three methodologies, with SITAR exhibiting the latest aPHV and largest PHV estimates.

Conclusion: Each statistical methodology has a number of advantages when used for specific purposes. For modelling growth in individuals, as one would in paediatric clinical practice, we recommend the use of the 5th order polynomial methodology due to its parameter flexibility.

Citing Articles

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The combined effects of growth and maturity status on injury risk in an elite football academy.

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Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton.

Middleton K, Duren D, McNulty K, Oh H, Valiathan M, Sherwood R Sci Rep. 2023; 13(1):19294.

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