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Bioequivalence Testing
Published in Sarfaraz K. Niazi, Handbook of Pharmaceutical Manufacturing Formulations, Third Edition, 2019
The purpose of bioavailability studies is to demonstrate therapeutic equivalence. However, depending on the mechanism of action, more meaningful comparisons can be made from such parameters as peak plasma concentration or the time to reach peak plasma concentration. For example, in the case of antibiotics, it is important to know how soon the minimum inhibitory concentration is reached and maintained. The choice of single-dose versus multiple-dose study depends on the mechanism of drug action. For example, antidepressants such as imipramine show delayed action, a characteristic of many psychotropic and antihypertensive agents. In these instances, a new product should be judged for its quality from repeated administration, because in these examples, the peak concentration or time to peak concentration is relatively unimportant. It is therefore important to isolate the clinically important parameter, but in all instances, the area under the curve (AUC) must be monitored, since it represents the proportionality to the total amount of drug eliminated from the body and hence, absorbed.
Combined models of artificial immune systems
Published in Waldemar Wójcik, Andrzej Smolarz, Information Technology in Medical Diagnostics, 2017
V.I. Lytvynenko, W. Wójcik, A. Smolarz, B. Suleimenov, M. Junisbekov
The clonal negative selection was benchmarked using the standard set of test problems, in comparison with the procedure of negative selection and combined models. To evaluate the obtained groups of binary classifiers, ROC-curves are used (Fig. 10.6 and Fig. 10.7). In this case AUC (the area under curve) is the numerical index area under the curve and the percentage of correctly recognised objects in the test set.
Cross-validation of cut-points in preschool children using different accelerometer placements and data axes
Published in Journal of Sports Sciences, 2022
Teatske M. Altenburg, Lotte de Vries, Rianne op den Buijsch, Emma Eyre, Alexandra Dobell, Michael Duncan, Mai J.M. Chinapaw
Descriptives of demographic information (means ± SD) were calculated. For the cross-validation of cut-points, accelerometer data in 30-s epochs were used to match the OSRAC-P observation interval. The area under the curve (AUC) was calculated using receiving operating characteristic (ROC) curve analysis (Jago et al., 2007), representing a measure of precision. An AUC of under 0.70 is considered as poor, 0.70–0.79 fair, 0.80–0.89 good and 0.90–1.0 excellent (Hanley & McNeil, 1982). To obtain an indication of the bias in time estimates, average time (in minutes; and 95% confidence intervals (95% CI)) classified as sedentary, light physical activity or MVPA was calculated for observational data and accelerometer data, representing a measure of accuracy. Analyses were performed in SPSS version 24 (SPSS Inc, Chicago, IL, USA).
Person anomaly detection-based videos surveillance system in urban integrated pipe gallery
Published in Building Research & Information, 2021
Laisong Kang, Shifeng Liu, Hankun Zhang, Daqing Gong
The true positive rate (TPR) is the rate that the scoring function correctly classifies the anomalous instance as anomaly.where is a threshold, is the expectation and denote an indicator function with the condition . When is true, , otherwise . Meanwhile, the false positive rate (FPR) is the rate that the scoring function misclassifies a random normal instance from as anomaly.The AUC is the area under the curve formed by plotting pairs for all thresholds . Specifically, the integral form of AUC can be written as
An effective strategy for the analysis of response profiles
Published in Quality Engineering, 2020
The statistics A and B have important interpretations. A measures the “level” of the profile and B measures the profile “shape”. A also measures the “area under the curve”. In the field of pharmacokinetics, the area under the curve (AUC) is the drug concentration in blood plasma vs. time. In practice, the drug concentration is measured at certain discrete points in time and the trapezoidal rule is used to estimate AUC (Bolton and Bon 2004). The result is identical to the average of the profile values (Wang et al. 2016).