• What is the condition (disease) of interest. 
• What is the test of interest 
• What is the comparison test (gold standard) of interest
• What do out want to know about the test, e.g. the test related "outcome."  Usually what we want to know is the test's  accuracy

For information on screening tests see  cpmcnet. 
 

For a simple approach try the following

• Enter a search filter for diagnostic testing.  

Type:  exp "sensitivity and specificity"/  in the ovid search field
• Search the test
• Search the disease
• Combine these (Boolean AND): 1 AND 2 AND 3
• See an example 

• Accounting:  Do the numbers add up?  The article should provide a clear description of patient flow through the diagnostic protocol and all patients should be accounted for
• Methods: reporting Were the methods for performing the test described in sufficient detail to permit replication of the test in your setting?
• Blinding:  Was there an independent, blind comparison with a reference standard? E.g. the person interpreting the study test was blinded to results of the gold standard test, and vice versa.   How good was the method of blinding?  Lack of blinding may result in several forms of Review bias.
• Reference standard (gold standard) comparison:  The gold standard must be valid. All patients should be investigated by both tests.
• Precision:  Confidence intervals on outcomes (sensitivity, specificity etc.) should be presented.  If not, calculate them.
• Bias
• Spectrum Bias:   Did the study sample include an appropriate spectrum of patients and was the population similar to yours?  If not, how would you expect the test to perform in your setting
• Verification Bias:   Did the results of the test being evaluated influence the decision to perform the gold standard test?
• For interested readers: 

 

Likelihood ratios (LR) are the most valuable way to express a test's potential clinical impact. 

• The LR is a statistical expression. It describes how a test result effects the probability that a disease or condition is present.  Test information is only valuable if it changes the probability of disease enough to alter treatment or diagnosis.  Although the LR can be derived from sensitivity and specificity, those expressions alone don’t provide information on the effect of a result on disease probability.

• The LR for a positive result = sensitivity/ (1-specificity)
• The LR for a negative result = (1-sensitivity)/specificity
• Confidence Intervals should be provided for all of the above mentioned result measurements.   If not provided, provided calculate confidence intervals on the LR.

 

• Is your patient  typical of a study patient?  If not ,  the results may not be applicable. See discussion on spectrum bias above.

• Will the reproducibility of the test result and its interpretation be satisfactory in your setting?  If  the test was conducted or interpreted by individuals with specialized training, on different equipment, or in a different environment, can you expect similar results?

• Tests which require subjective interpretation may be problematic. Look for an analysis or discussion of inter-rater reliability (kappa), or perform a separate search on this. 

 

• As few tests are perfect, the possibility that your test result is false (FP or FN) must be considered. Thus, most tests don’t rule in or rule out disease, they change the probability of disease.

• Consequently the real questions are 1) what is the probability of disease if the test result is positive (or negative)?  and 2) will a positive or negative result change the probability of disease enough to result in an important change in diagnosis or management.

• To answer these questions you must determine (or estimate) the pre-test probability of disease, and determine the likelihood ratio's for  positive and negative test results. 

• When you have determined these values you can determine the post-test probability of disease, then make a value judgment as to the potential benefits or risks of testing. Use the LR nomogram from CEBM.