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The performances of the models were measured using area under the receiver operating characteristic curve (AUC) [ 40], which is frequently used to evaluate prediction models in the biomedical informatics field [ 41].
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The wind pressure on various surfaces of the building models are measured using the Baratron pressure gauge from MKS Corporation Ltd.
Hazard ratios (HR) together with 95% confidence intervals (CI) were estimated, and the performance assessment of the different survival models was measured using the concordance index (C-index).
Performance of the models was measured using precision and recall after application of the various class-specific binary thresholds (previously calculated) for each target.
The calibration of the models was measured using calibration curves [ 39].
The discriminant performance of models was measured using the time-dependent area under the curve (AUC) using the SurvivalROC R package 32.
Oxygenation in the established tumour models was measured using the OxyLite pO2 system (Oxford Optronix Ltd, Oxford, UK) (Young et al, 1996; Collingridge et al, 1997; Seddon et al, 2001).
Performance of the models was measured using an R2 value, Clarke error grids (CEG) and rate-error grid analysis (R-EGA).
The discriminate ability of the three models was measured using discrimination metrics including sensitivity, specificity, Youden index, positive predictive value (PPV), negative predictive value (NPV), Brier score and AUC values (Table 3).
The level fluctuation and the fluid flow in the pool of the water model were measured using the level detector and the 3D-LDV (laser Doppler velocimetry) technology.
The effects of bifurcation angle on the volume flow division ratio between the superficial femoral and the profunda femoris sections of the model were measured using a transit time flowmeter.
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