If diagnostic reliability is defined as the likelihood of a patient receiving the same diagnosis at different hospitals or clinics, or the likelihood of different studies recruiting similar patients, then the test-retest method provides a more meaningful estimate of diagnostic reliability (Kraemer et al.). The kappa statistic is used to measure interrater agreement for categorical data. It ranges from -1 to +1, with 0 indicating no agreement beyond chance and 1 perfect agreement. A kappa value of 0.8 or higher is considered good agreement. Kappa can be calculated using Stata's "kappas" command. For example, let's say that patients were asked whether they had diabetes by way of a questionnaire administered at both visits. If 20% of patients answered yes at visit 1 and 15% at visit 2, then the kappa would be 0.76. Kappas are useful in comparing results across studies but should not be interpreted in isolation. For example, if one study found a kappa of 0.75 while another found only 0.4, it may be because the first study included cases where agreement was high while the second included cases where agreement was low. There are several other statistics that can be used to measure reliability. Two commonly reported measures are sensitivity and specificity. Sensitivity refers to the ability of a test to identify those patients who actually have the disease; specificity refers to the ability of a test to avoid identifying those patients who do not have the disease.