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General linear modelling (GLM) for repeated measures was used to test whether differences between the same measures of pressure on the same patients changed significantly over time.
It has been suggested that repeatability of HRV measures from 10-sec recordings is high when the mean from two or three records is used, and correlations between the same measures from 10 sec and 6 min were quite high (Schroeder et al. 2004).
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In this respect, it contrasts with the same study that found a negative relationship between the same measure of TE content and average levels of nucleotide polymorphism [ 43].
Any factor that contributes to less than perfect correlation (i.e. relative change) between the same measure at two time points, per definition contributes to RTM [ 14].
In general, the correlation of a given measure with patient satisfaction (SAMS-S) was also lower than the correlation between the same measure and parent satisfaction (SAMS-P, Table 6).
Assuming equal numbers of participants in intervention and control arms, a correlation of 0.60 between the same measure collected at two different points in time, and two follow-up waves, a total sample size of 146 was calculated for an effect size of 0.40, an alpha level of 0.05, and a power of 0.80 [ 19].
The secondary endpoints were differences on the same measures between treatment and waiting-list group 26 weeks after baseline.
The magnitude of the decrease in team members' levels of job stress and job satisfaction was similar to the increase in job stress reported by UK hospital consultants between 1994 and 2002 using the same measures[ 3].
We used multiple linear regression to estimate the association between PFOA and birth weight, using the same measures of estimated PFOA exposure described for the categorical outcomes.
The ICC by resampling method was 0.71 with lower and upper confidence limits of 0.53 to 0.88. Figure 4 summarizes the standard deviation of repeated measures to demonstrate the measurement variability for keratometric power and corneal anterior radius, and Fig. 5 summarizes the relationships between the studied imaging devices for the same measures.
This can be used to disambiguate the joint effects of measurement biases and substantive differences that are evident between different populations who use the same measure [ 18].
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