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Suppose, for example, I have 200 individuals referred for assessment in my glaucoma clinic, 100 of the subjects referred are assessed using a VFA, while the other 100 are assessed using a new imaging technology.
The scenarios are assessed using a multicriteria analysis (MCA) methodology, as outlined in Fig. 1.
The capabilities of model-robust supersaturated designs for model discrimination are assessed using a model-discrimination criterion, the subspace angle.
As discussed in Section 4, these aspects of fit are assessed using a posterior predictive fit criterion (Laud and Ibrahim 1995).
The model parameters in the modified time function and the new correction factor are assessed using a computational intelligence method called particle swarm optimization.
Each of these areas of risk are assessed using a bespoke method and at all stages of the technology's lifecycle process, from design through to decommissioning.
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Admission data are assessed using an unpaired t-test.
Linearity was assessed using a residual plot.
The categorical data were assessed using a chi-square test.
Retention capacity was assessed using a noninvasive fluorescence imaging system.
Size distribution was assessed using a Malvern instrument.
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