Exploring individual change.

Abstract

In the analysis of the impact of clinical interventions, the received wisdom has been that posttreatment scores, with pretreatment scores equated by random assignment or statistically partialed out, should be used to evaluate treatment outcomes. However, posttreatment scores are not generally more reliable than, nor equivalent to, change scores, even with pretreatment scores partialed out of both. Moreover, there are data-analytic methods that indicate how individual patients change, in terms of response curves over time, rather than indicate only how much groups change on the average. These methods take researchers back to the individual data that they ought to use for choosing the specific models of change to be used. To maximize relevance for clinical practice, the results of treatment research should always be reported at this most disaggregated or individual change level, as well as, when appropriate, at more aggregated statistical levels.