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We analyzed sensitivity by changing the covariance structure to first-order autocorrelation.
We analyzed sensitivity, specificity and area under the ROC curve (AUC-value) to compare the performance of various modelling techniques by 10 × 10 cross-validation.
In all iterations, we calculated and analyzed sensitivity and specificity compared to the reference standard and examined discordant pairs for data which could be used to improve the electronic search accuracy.
Finally and in the absence of standardized national prices, we analyzed sensitivity t o laboratory tests by using laboratory test charges instead of laboratory test costs as used in the base case.
This resulted in 1/17 cancers misclassified (the same tumor that was misclassified when all 19 cancers were analyzed; sensitivity 94%) and 4/17 controls misclassified (the same 4 controls that were among the 9 misclassified benign cases; specificity 76.5%).
When these tests were evaluated against the higher-quality reference standard (ie when multiple samples were analyzed), sensitivity was lower for microhaematuria (71% vs 76%) and proteinuria (49% vs 68%) than with a lower-quality reference standard.
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An analytical method of analyzing sensitivity is presented.
The adjoint sensitivity analysis method (adjoint method) is a way to analyze sensitivity very quickly.
In this research, in order to analyze sensitivity and graphical visualization of results, PROMETHEE GAIA has been used.
This paper focuses on analyzing sensitivity of the method's parameters and the applicability in optimization problems.
Availability analysis platform is designed to calculate availability of target system and analyze sensitivity and performability as well.
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