Why are likelihood ratios weighted for prevalence? I notice LLR's are weighted for prevalence if the prevalence differs from 0.5 (see: http://vassarstats.net/clin2.html)
Can someone explain why this is? I'm a medical student, not a statistician, so I'll probably not be able to understand the answer if it's too theoretical.
Thank you.
 A: 
... weighted for prevalence if the prevalence differs ... Can someone explain why this is?

The diagnostic test provides a result, pass/fail for example, but the simple passing or failing doesn't match the outcome for specific members of the population; there are true positives, false positives, true negatives and false negatives - the percentage of each differs from the truth of each for specific members of the population.
So one would weigh fact with diagnostic result to obtain a more correct answer as to the applicability of the test outcome when applied to the final outcome.
See: Wikipedia's "Likelihood ratios in diagnostic testing".
See: "Variation of sensitivity, specificity, likelihood ratios and predictive values with disease prevalence" (May 15 1997), by H. Brenner and O. Gefeller:

"The sensitivity, specificity and likelihood ratios of binary diagnostic tests are often thought of as being independent of disease prevalence. Empirical studies, however, have frequently revealed substantial variation of these measures for the same diagnostic test in different populations. One reason for this discrepancy is related to the fact that only few diagnostic tests are inherently dichotomous.
...
We illustrate that variation with disease prevalence is typically strong for sensitivity and specificity, and even more so for the likelihood ratios. Although positive and negative predictive values also strongly vary with disease prevalence, this variation is usually less pronounced than one would expect if sensitivity and specificity were independent of disease prevalence.".

Also see: "Simplifying Likelihood Ratios" (Aug 17 2002), by Steven McGee:

"CONVENTIONAL APPLICATION OF LRS
How much does the finding of bulging flanks (LR = 2.0) argue for ascites, and how much does the finding of flank tympany (LR = 0.3) argue against it? To answer these questions using traditional methods, clinicians must first identify the pretest probability (or prevalence) of ascites in their practice and then perform 3 calculations. For example, if about 2 out of every 5 patients with abdominal distension have ascites, the pretest probability is 40%. The traditional way of applying the finding of bulging flanks (LR = 2.0) is to then convert pretest probability (P$_{pre}$) to pretest odds (O$_{pre}$), using O$_{pre}$ = P$_{pre}$/(1 − P$_{pre}$), then multiply the pretest odds (O$_{pre}$) by the LR for the finding to derive the posttest odds (i.e., O$_{post}$ = LR × O$_{pre}$), and then convert posttest odds back to posttest probability, using P$_{post}$ = O$_{post}$/(1 + O$_{post}$).
..."

Simplified: You can perform a test and get an answer indicating a specific outcome but the results must be weighed with post-operative prior results to accurately weigh the results provided by the test. If the test usually is TP, TN, FP or FN you need to consider that.
