Abstract
The validity of clinical diagnoses is a fundamental topic in clinical psychology, because now there are some political administrations, as the IOM or the U.K. government, which are focusing on best evidence-based practice in clinical psychology. The most problematic issue in clinical psychology is to avoid wrong diagnoses which can have negative consequences on individual life and on the utility of clinical treatments. In the case of diagnoses based on self-report tests, the diagnostic decision about individual health is based on the comparison between its score and the cutoff, according to the frequentist approach to probability. However, the frequentist approach underestimates the possible risks of incorrect diagnoses based on cutoffs only. The Bayesian approach is a valid alternative to make diagnoses on the basis of the scores from psychological tests. The Bayes' theorem estimates the posterior probability of the presence of a pathology on the basis of the knowledge about the diffusion of this pathology (prior probability) and of the knowledge of sensitivity and specificity values of the test. With all this information, it is possible to estimate the diagnostic accuracy of some self-report tests used for assessing depression. We analyzed the diagnostic accuracy of the most used psychological tests of depression (Zung's Self-Rating Depression Scale, Hamilton Rating Scale for Depression, Center for Epidemiological Studies for Depression and the Beck Depression Inventory), together with a new scale (Teate Depression Inventory) developed with the IRT procedure, by analyzing the published works in which data about sensitivity and specificity of these scales are reported. Except the TDI, none of these scales can reach a satisfactory level of diagnostic accuracy, probably for the absence of an optimal procedure to select test items and subjects with clearly defined pathological symptoms which could allow the reduction of false positives in test scoring.