We adopt the popular D-optimality criterion as providing an appropriate objective function for designing our experiments, since this leads to estimates with maximum precision, subject to valid assumptions about parameter values.
n = number of subject with valid answer.
In each subject, three valid measures by B & K and PiCO+ were obtained.
Up to three missing item values were imputed by value substitution based on each subject's valid responses to NPAD-d items.
Among participants at baseline, 992 subjects had valid measurement of POPs; after excluding 3 subjects without information on fasting blood glucose, 989 subjects constituted the study sample for the cross-sectional analyses.
Subjects were valid for inclusion in the safety analysis (91 subjects) if they received at least one dose of study medication.
Subjects with valid creatinine levels from both sampling periods had their metabolite levels averaged over the two samples; subjects with a valid creatinine level from only one sample contributed metabolite data from only that sample.
Power analysis (power 0.8; α 0.05; detectable difference 8.0; standard deviation 5.1) indicated a sample size requirement of 15 subjects for valid comparisons.
The possible link between collective word intuition and word segmentation standard, as well as the fact that previous studies failed to recruit enough subjects for valid results lead to our proposal to use the NLP technique of crowdsourcing to tackle this issue.
There were a further 860 subjects with valid acute stroke codes ever recorded, affirmed by three or four raters, in whom the valid acute stroke code was not an index code.
