Context Unlike other areas of medicine, psychiatry is almost entirely dependent on patient report to assess the presence and severity of disease; therefore, it is particularly crucial that we find both more accurate and efficient means of obtaining that report.
Objective To develop a computerized adaptive test (CAT) for depression, called the Computerized Adaptive Test–Depression Inventory (CAT-DI), that decreases patient and clinician burden and increases measurement precision.
Design Case-control study.
Setting A psychiatric clinic and community mental health center.
Participants A total of 1614 individuals with and without minor and major depression were recruited for study.
Main Outcome Measures The focus of this study was the development of the CAT-DI. The 24-item Hamilton Rating Scale for Depression, Patient Health Questionnaire 9, and the Center for Epidemiologic Studies Depression Scale were used to study the convergent validity of the new measure, and the Structured Clinical Interview for DSM-IV was used to obtain diagnostic classifications of minor and major depressive disorder.
Results A mean of 12 items per study participant was required to achieve a 0.3 SE in the depression severity estimate and maintain a correlation of r = 0.95 with the total 389-item test score. Using empirically derived thresholds based on a mixture of normal distributions, we found a sensitivity of 0.92 and a specificity of 0.88 for the classification of major depressive disorder in a sample consisting of depressed patients and healthy controls. Correlations on the order of r = 0.8 were found with the other clinician and self-rating scale scores. The CAT-DI provided excellent discrimination throughout the entire depressive severity continuum (minor and major depression), whereas the traditional scales did so primarily at the extremes (eg, major depression).
Conclusions Traditional measurement fixes the number of items administered and allows measurement uncertainty to vary. In contrast, a CAT fixes measurement uncertainty and allows the number of items to vary. The result is a significant reduction in the number of items needed to measure depression and increased precision of measurement.