Centre for Statistical Methodology (CSM)
Design and Analysis of Dependent Data Theme - SEMINAR
 
Friday 9 March 2012, 1:00-2:00 pm
LG9, Keppel Street

Modelling human growth

Prof Noel Cameron
Centre for Global Health and Human Development, School of Sport, Exercise and Health Sciences, Loughborough University, UK



Note:External attendees, please come to the Keppel Street reception and sign

 


ABSTRACT
The pattern of human growth is described in terms of magnitude and rate of change or distance and its derivatives, velocity and acceleration. It was first described, by the Comte de Buffon during the Enlightenment of the 18th Century, through serial anthropometric measurements of height. The resulting pattern of growth related to the frequency of measurement; the more frequent the measurements, the more detailed the pattern. The American longitudinal studies initiated in the first half of the 20th Century and the post WWII European studies provided a wealth of data, acquired at frequent intervals, that leant themselves to a more detailed consideration not only of growth in height but also of a variety of other dimensions. Mathematical modelling of the pattern of human growth during the 1950s and 1960s was confined to either non-parametric models or rather simple polynomial functions involving few parameters such as the Count and Jenss-Bayley curves that described growth in infancy and childhood.
 Access to significant computing power from the 1970s onwards allowed the generation of complex parametric models composed of multiple polynomial and logistic functions that could describe the complete curve of growth from birth to adulthood. Michael Preece in London and Darryl Bock in Chicago provided the most enduring models with the Preece-Baines Model 1 (PB1) and the Bock Triple-logistic (BTl). All such models were defined and constrained by the number of parameters required to accurately and parsimoniously describe the pattern of growth; five in PB1 and nine in the BTl. Each parameter ideally required biological explanations which were at times difficult to elucidate. Multilevel modelling demonstrates the latest advancement in growth modelling allowing the identification of independent inter-group effects whilst also controlling for the effects of growth and maturation of the individual.

For more information on the Centre for Statistical Methodology at LSHTM, see http://csm.lshtm.ac.uk.


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