False negative predictive value

For understanding sensitivity, specificity, false negative predictive value, and false positive predictive value, consider the four cells and two column margins of Table 2, where individuals are cross-classified with respect to their true sero-status versus observed sero-status. Sensitivity is represented by Se = / (testing outcome + truth is +) and specificity is Sp = / (testing outcome - truth is -). With these definitions and with p denoting the probability of an individual having... 

Third, for forensic cases in which there is a question of poisoning with ethanol, a sample of blood could be analyzed for FAEE quantitation to assess prior ethanol administration. It is not yet known if there are any causes for a false-positive FAEE test or a false-negative FAEE value. It is possible that ingestion of certain medications or foodstuffs will make the FAEE test falsely positive, and it is also possible that interferences exist that block the detection of FAEE. No causes of false-positive or falsenegative tests have been yet identified, but there is always a possibility that one will appear as more research is done in the field. In addition, one cannot use the level of blood FAEE 24 h after drinking alcohol to predict the peak blood ethanol concentration much earlier. 

Sn being the sensitivity (or the true positive rate, TPR or recall), Sp the specificity (or the true negative rate, TNR), FNR the false negative rate, FPR the false positive rate, PPV the positive predictive value (or precision), and NPV the negative predictive value. 

Because of its high sensitivity in detecting malignant lesions and high negative predictive value PET has already established its role in initial staging and monitoring of therapy response for various cancers. False positive results have been described in inflammatory disease [38] since FDG uptake is not cancer specific. The standardized uptake value (SUV) pro-... 

The low predictive values of pharmacogenetic tests for most polymorphic variants means there will be false positive (when PPV is low) and false negative (when NPV is low) test results. Both reduce the clinical utility of the tests. There are a number of reasons for the reduced predictive values. 

The approach of CAESAR is quite similar to that of DEMETRA. So far good results have been obtained for the bioconcentration factor (BCF) in fish, superior to those of other models. Models have been tested with an external validation set. The model gives as prediction the BCF as continuous value, but it has been optimized to reduce false negatives. In the specific case of the REACH legislation, which is the target of the project, bioaccumulative chemicals are defined if the BCF value is above 3.3 in logarithmic unit. This shows another example of the specificity of the models, because different threshold may apply in other countries. 

For the models providing yes/no predictions only, it is important to point out that false negatives and false positives depend on the defined cut-off value to distinguish active from inactive compounds. As the cut-off value is lowered it is likely that error will increase even for a well-designed and executed assay. The increased experimental error in close proximity to the cut-off value will... 

Equation (3), which is an application of Bayes theorem, is referred to as the Positive Predictive Value. The parameter p is unknown but believed to be very small (<0.01) for large virtual libraries. 1 - p is the power (or 1 - type II error, where ft is the false negative error rate) and a is the type I error, also called the size of a test in the hypothesis testing context, or the false positive error rate. The last equation defines the probability that a molecule is determined to be a hit in a biochemical assay given that the virtual screen predicts the molecule to be a hit. This probability is of great interest because it is valuable to have an estimate of the hit rate one can expect for a subset of molecules that are selected by a virtual screen. 

---