Interpretation of diagnostic data: 3. How to do it with a simple table (part B). (1983)
The following guidelines are useful if you want to "do it with a simple table" (Table IV): First, identify the sensitivity and specificity of the sign, symptom or diagnostic test you plan to use. Many are already in the literature, and subspecialists should either know them for their field or be able to track them down for you. Depending on whether you are considering a sign, a symptom or a diagnostic laboratory test, you will want to track down a clinical subspecialist, a radiologist, a pathologist and so on. Start your table with a total of 1000 patients, as shown in location (a + b + c + d) of panel A. Using the information you have about the patient before you apply the diagnostic test, estimate the patient's pretest likelihood (prevalence or prior probability) of the target disorder -- let's say 10%. Take this proportion of the total (100) and place it in location (a + c); the remaining 900 patients go in location (b + d) (panel B). Multiply (a + c) (100) by the sensitivity of the diagnostic test (let's say 83%) and place the result (83) in cell a and the difference (17) in cell c; similarly, multiply (b + d) (900) by the specificity of the diagnostic test (let's say 91%) and place the result (819) in cell d and the difference (81) in cell b (panel C). If (a + b) and (c + d) do not add up to 1000, you will know you have made a mistake. You can now calculate the positive predictive value, a/(a + b), and the negative predictive value, d/(c + d), as shown in panel D. You have now reached a level of understanding a fair bit beyond the rule-in/rule-out strategy discussed in part 1 of our series. Furthermore, you can already do more than most clinicians, so you may want to stop here, at least for a while. On the other hand, you may want to go further and learn how to handle slightly more complex tables with multiple cut-off points. In the next article you will find more powerful ways to take advantage of the degree of positivity and negativity of diagnostic test results.