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The BP map in each subjects was generated by Pmod 2.9 (Pmod Technologies Ltd., Switzerland), based on the simplified reference model [69], [70] using the ipsilateral cerebellum of the stimulated hemisphere as a reference region.
A population dataset of 50 subjects was generated using Monte-Carlo simulation in ADAPT 5.
A randomization list for the subjects was generated before the trial.
Genotype data for subjects was generated on two different array platforms, 105 individuals on Illumina CytoSNP (299,173 SNPs) and 96 on Illumina 300k chips (300,299 SNPs).
The database of virtual subjects was generated by varying arterial and cardiac parameters of the blood flow model by a range of physiological values for healthy subjects taken from the literature.
The means and coefficients of variation for each parameter are summarized in Table 2. To determine the prevalence of severe neutropenia in specific populations by in silico simulation, each parameter in the PK/PD model for virtual subjects was generated as described.
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The error-free, baseline TACs (19 framin/90 min) simulating the target ROI of the NC and AD subjects were generated by using the 4P model with parameter set (K 1 = 0.180 mL/g/min, k 2 = 0.180/min, k 3 = 0.018 and 0.036/min for the NC and AD subjects, respectively, and k 4 = 0.018/min; typical values for [11C]PIB) and averaged (N = 20) input function of [11C]PIB.
Three independent bootstrap sets with 175, 350 and 350 subjects were generated.
P-values to compare proportions between case and control subjects were generated using the conditional logistic regression model.
For each covariate, if continuous, two subjects were generated with extreme covariate values (5th and 95th percentile); if categorical, one subject from each category was created, with other covariates fixed at median (continuous) or at certain category (categorical).
During the study period, all incident falls reported by ward nurses were carefully reviewed by research staff on the next day, and a matched control subject was generated according to the age, sex, diagnosis, and pre-event length of stay.
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