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Where outcomes are unequivocal (life or death; being able to walk v being paralysed) clinicians, researchers, and patients find it easy to speak the same language in evaluating results. However, in much of mental health work initial states and outcomes of treatments are measured on continuous scales and the distribution of the “normal” often overlaps with the range of the “abnormal.” In this situation, clinicians and researchers often talk different languages about change data, and both are probably poor at conveying their thoughts to patients.
Researchers traditionally compare means between groups. Their statistical methods, using distributions of the scores before and after treatment to suggest whether change is a sampling artefact or a chance finding, have been known for many years.1 By contrast, clinicians are more often concerned with changes in particular individuals they are treating and often dichotomise outcome as “success” or “failure.” The number needed to treat (NNT) method of presenting results has gone some way to bridge this gap but often uses arbitrary criteria on which to dichotomise change into “success” and “failure.” A typical example is the criterion of a 50% drop on the Hamilton Depression Rating Scale score. A method bridging these approaches would assist the translation of research results into clinical practice.
Jacobson et al proposed a method of determining reliable and clinically significant change (RCSC) that summarises changes at the level of the individual in the context of observed changes for the whole sample.2, 3–5 Their methods are applicable, in one form or another, to the measurement of change on any continuous scale for any clinical problem, although they have been reported primarily in the psychotherapy research literature.
The broad concept of reliable and clinically significant change rests on 2 questions being addressed at the level of each …
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