SITAR growth curve analysis - a statistical method to assess longitudinal growth and development data

The pattern of growth in individual children, as represented by their growth curve of successive measurements (e.g. weight or height) plotted against age, may be informative about their circumstances at earlier ages, or conversely it may predict their risk of disease later in life, for example heart disease, stroke or type 2 diabetes. To investigate this it is useful to summarise the shape of each individual's growth curve in a simple way, so that its shape can be related to their earlier or later experiences. The SITAR method of growth curve analysis is a recently developed approach that does just this - it summarises each individual growth curve as just three numbers, reflecting respectively their size (i.e. whether they are tall or short, or heavy or light, as compared to others), their growth tempo (i.e. whether they are relatively mature or delayed in their timing of growth, e.g. when girls reach menarche), and velocity (how fast they are growing compared to others, e.g. in puberty). So for example an individual might be relatively tall, with early puberty, and growing relatively fast. In addition SITAR estimates a smooth average growth curve against which each individual's growth pattern can be compared. The surprising thing is that this model explains nearly all of the variability in growth between individuals, so that their size, tempo and velocity can be viewed as a complete summary of their growth pattern. Put another way, when adjusted for size, tempo and velocity all their growth curves look the same (the SITAR acronym mean Super-Imposition by Translation And Rotation). It is then simple to relate the values of size, tempo and velocity in individuals to their earlier or later experiences, and to look for links with their growth pattern.

The SITAR method of growth curve analysis summarises individual growth patterns of e.g height or weight in three parameters (size, tempo and velocity) that are estimated as subject-specific random effects, plus a cubic spline estimate of the average growth curve. It explains over 95% of the age-specific variance in the outcome measure, and as such is an effective summary of individual growth patterns. The subject-specific random effects can then be related to earlier growth-affecting exposures or later health outcomes. Thus it is relevant in translational medicine and life course epidemiology. The aims of the project are (i) to further develop the SITAR methodology, (ii) to apply it to a series of existing growth studies, and (iii) to develop an existing SITAR software library in the R statistical language for its wider dissemination. Methodological developments will include applying the method to developmental as well as growth outcomes, and allowing the fitting of multiple mean curves rather than just one. The application studies are collaborations involving longitudinal data covering height, weight, spirometry and brain growth in childhood and puberty. The R library allows other statisticians and epidemiologists to apply the method relatively simply.

Who will benefit from this research, and how?

o Academia
All researchers, national and international, interested in the analysis of longitudinal growth will find SITAR of interest, as it will allow them to summarise individual growth patterns in a simple way and then link the pattern to other aspects of the individual's health and lifestyle.

o Public Sector
National policy makers concerned with the high and rising levels of childhood obesity will want to explore the idea from SITAR that overweight children have an advanced maturational age, and look for lifestyle interventions that might counteract this accelerated age pattern.

o Business/Industry
The international pharmaceutical industry will value a statistical tool that simplifies and sharpens the assessment of growth-affecting treatments. SITAR's efficient use of data will mean that clinical trials involving growth outcomes need not be as large as before, and hence may reach conclusions more quickly and certainly more cheaply. This in turn should make cheaper the development of new growth-affecting drugs.

o Parents and children
Parents and children worldwide will benefit from improved growth assessment through the development of more effective evidence-based growth charts, particularly in puberty.