The Development of Circadian Rhythms in Human Infants

test that each infant‟s height and slope differed from each other; more than what
would be expected at random.
The height and slope were correlated and this was allowed for in the model. The
random effects modelling were therefore used to examine the relationship between
which measure was being analysed (e.g. night time melatonin) and postnatal age (in
the given example looking for an overall increase or decrease in night melatonin with
increasing age).
Further detail on the use of linear regression and random effects modelling are given
in the relevant results chapters.
The first stage of the gene expression data analysis was to plot the sinusoidal curve
that best fitted the observed data for each baby week. The goodness of fit for each
graph for each baby week was ascertained in two ways. Firstly the amplitude was
calculated; this was a separate value for each baby week. We chose amplitude as the
best indicator of goodness of fit as it showed the strength of the relationship and is
read in the same units as gene expression. It gave a value for how well the
H3f3b/GAP ratio fitted a diurnal pattern. Secondly as a secondary measure and as a
robust check for goodness of fit, a commonly cited statistical measure R-squared was
also used. This was also calculated for each baby week, so any given baby has
several values for each week of measurement. R-squared showed the proportion of
the total variation (variance) in the observed values that is explained by the fitted
curve.
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