Abstract
Longitudinal studies are permeating clinical trials in psychiatry. Additionally, in the same field, rating scales are frequently used to evaluate the status of the patients and the efficacy of new therapeutic procedures. Therefore, it is of utmost importance to study the psychometric properties of these instruments within a longitudinal framework. In the area of depression, the Hamilton depression rating scale (HAMD) is regularly used for antidepressant treatment evaluation. However, the use of HAMD has not been exempted from criticism what has lead to the development of new scales that are expected to be more sensitive for change, such as the Montgomery–Åsberg depression rating scale (MADRS). In general, the reliability of these scales has been extensively studied by using classical methods for reliability estimation, developed for specifically designed reliability studies. Unfortunately, the settings customarily considered in these reliability studies are usually far from the practical conditions in which these scales are applied in clinical trials and practice. In the present paper, we assess the reliability of these instruments in a more realistic scenario thereby using longitudinal data coming from clinical studies. Nowadays, newly developed methodology based on an extended concept of reliability, allows us to use longitudinal data for reliability estimation. This new approach not only enables to avoid bias by offering a better control of disturbing factors but it also produces more precise estimates by taking advantage of the large sample taking sizes available in clinical trials. Further, it offers practical guidelines for an optimal use of a rating scale in order to achieve a particular level of reliability. The merits of this new approach are illustrated by applying it on two clinical trials in depression to assess the reliability of the three outcome scales, HAMD, MADRS, and the Hamilton anxiety rating scale (HAMA).
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