Search results
Results from the WOW.Com Content Network
The log diagnostic odds ratio can also be used to study the trade-off between sensitivity and specificity [5] [6] by expressing the log diagnostic odds ratio in terms of the logit of the true positive rate (sensitivity) and false positive rate (1 − specificity), and by additionally constructing a measure, :
Sensitivity and specificity values alone may be highly misleading. The 'worst-case' sensitivity or specificity must be calculated in order to avoid reliance on experiments with few results. For example, a particular test may easily show 100% sensitivity if tested against the gold standard four times, but a single additional test against the ...
If there were very different values of resistance present a value closer to these might be a better choice. A lumped-element circuit which may be analyzed using a Smith chart. Smith chart with graphical construction for analysis of a lumped circuit. The analysis starts with a Z Smith chart looking into R 1 only with no other components present.
Figure 2. Sampling-based sensitivity analysis by scatterplots. Y (vertical axis) is a function of four factors. The points in the four scatterplots are always the same though sorted differently, i.e. by Z 1, Z 2, Z 3, Z 4 in turn. Note that the abscissa is different for each plot: (−5, +5) for Z 1, (−8, +8) for Z 2, (−10, +10) for Z 3 and ...
This index correlates well with glucose clamp studies (r = 0.78), and is useful for measuring insulin sensitivity (IS), which is the inverse of insulin resistance (IR). It has the advantage of that it can be obtained from a fasting blood sample, and is the preferred method for certain types of clinical research.
IR is insulin resistance and %β is the β-cell function (more precisely, an index for glucose tolerance, i.e. a measure for the ability to counteract the glucose load). Insulin is given in μU/mL. [7] Glucose and insulin are both during fasting. [2] This model correlated well with estimates using the euglycemic clamp method (r = 0.88). [2]
Bacteria are marked as sensitive, resistant, or having intermediate resistance to an antibiotic based on the minimum inhibitory concentration (MIC), which is the lowest concentration of the antibiotic that stops the growth of bacteria. The MIC is compared to standard threshold values (called "breakpoints") for a given bacterium and antibiotic. [28]
In applied statistics, the Morris method for global sensitivity analysis is a so-called one-factor-at-a-time method, meaning that in each run only one input parameter is given a new value. It facilitates a global sensitivity analysis by making a number r {\displaystyle r} of local changes at different points x ( 1 → r ) {\displaystyle x(1 ...