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Sensitivity and Specificity

rewrite this to use TP/FP/FN/TN

give examples. ..

Sensitivity is the ability of a test to find cases, and is represented by A / (A+C)
Specificity is the ability of a test to avoid false positives, or D / (B+D)
The positive predictive value is the ability of the test to correctly label people who test positive, or A / (A+B)
The negative predictive value is the ability to correctly label people who test negative, or D / (C+D)

The role of prevalance in these things

Prevalence, or the number of people in a population who have the condition at any one time, increases the PPV but decreases the NPV. As prevalence increases, A and C go up as B and D go down, with the opposite happening for decreased prevalence.

Prevalence has no effect on a test's sensitivity or specificity. S and S are not as useful measures when prevalence is quite low; PPV and NPV are more important.

Likelihood Ratio

A l ikelihood ratio is the chance that a specified test result would be expected in a patient with the condition of interest, versus a patient without the condition.

In other words, the observed results are X times more likely to be seen in someone with, as opposed to without, the disease.

It is determined by [A / (A+C)] / [B / (B+D)], which is also [sensitivity / (1-specificity)]

Post-test probability (which is the same as PPV for dichotomous tests) can be determined using pre-test probability and a nomogram. Confidence intervals around the LR show are helpful and lead to a fairly broad range of possible post-test probabilities.