Estimating the probability of a diagnosis

[[abstract]]Sensitivity and specificity describe the accuracy of a test. In a clinical setting, we do not know who has the disease and who does not - that is why diagnostic tests are used. We would like to be able to estimate the probability of disease based on the outcome of one or more diagnostic tests. The following measures address this idea. Prevalence is the probability of having the disease, also called the prior probability of having the disease. It is estimated from the sample as (a+c)/(a+b+c+d). Positive Predictive Value (PV+) is the probability of disease in an individual with a positive test result. It is estimated as a/(a+b). Negative Predictive Value (PV - ) is the probability of not having the disease when the test result is negative. It is estimated as as d/(c+d). In the FNA study of 114 women with nonpalpable masses and abnormal mammograms, prevalence = 15/114 = 0.13 PV+ = 14/(14+8) = 0.64 PV - = 91/(1+91) = 0.99 Thus, a woman's prior probability of having the disease is 0.13 and is modified to 0.64 if she has a positive test result. A women's prior probability of not having the disease is 0.87 and is modified to 0.99 if she has a negative test result. If the disease under study is rare, the investigator may decide to invoke a case-control design for evaluating the diagnostic test, e.g., recruit 50 patients with the disease and 50 controls. Obviously, prevalence cannot be estimated from a case-control study because it does not represent a random sample from the general population