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A test which reliably detects the presence of a condition, resulting in a high number of true positives and low number of false negatives, will have a high sensitivity. This is especially important when the consequence of failing to treat the condition is serious and/or the treatment is very effective and has minimal side effects.
Screening all students every two days with a "low-sensitivity, high-specificity test" (such as a RAT) would be able to control an outbreak of COVID-19. [12] Mina et al theorized that higher-frequency screening using lower-sensitivity RATs may be more useful than lower-frequency screening using higher-sensitivity PCR tests because the former ...
They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition (such as a disease state) exists. The first description of the use of likelihood ratios for decision rules was made at a symposium on information theory in 1954. [ 1 ]
Specificity will generally be higher than sensitivity, especially when people have COVID-19 symptoms—in other words, false-negative COVID-19 tests are more likely than false positives.
The false positive rate (FPR) is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present. The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate.
It can be seen from the length of the arrows that, at low pre-test probabilities, a positive test gives a greater change in absolute probability than a negative test (a property that is generally valid as long as the specificity isn't much higher than the sensitivity). Similarly, at high pre-test probabilities, a negative test gives a greater ...
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, :
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.