The Development of Circadian Rhythms in Human Infants

A random effect modelling is an extension of linear regression, which has the
advantage that within the model itself, it is able to distinguish between babies. It takes
into account the structure of the data, i.e. for the group of infants who had data, each
infant was observed on a number of occasions on different weeks. It was used for the
more complex aspects of the analysis and was done by a specialist statistician,
following expert advice which was sought as to a suitable method to analyse the data
set. It is a hierarchical, multilevel, repeated measures modelling (Field, 2009). It was
used to investigate the change with age for several measurements for all infants. The
measurements included melatonin, cortisol, infant sleep and circadian gene expression
data (see sections 5.1.1, 6.1.1, 7.3.2 & 8.3). The data were fitted using STATA, a
comprehensive statistical package.
In the first part of the random effects modelling an average of each individual baby
slope was taken and it was assumed each infant had the same slope ANCOVA
(analysis of covariance). The height of the line (intercept) was calculated separately
for each infant‟s data; and an estimate of the slope given. In the next step of the
modelling, the slope of each infant remained the same for each infant, however the
estimates of the height of the line or the (height) of the y-intercept were adjusted to
give an inherent estimate of the amount of gene expression per baby at a given age
and a presumed normal distribution was drawn.
The final step was an extended random effects model which allowed the slope to
change for each infant. The height given (y intercept) was normally distributed and
the slope given was also normally distributed for each infant. It was then possible to
80