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Objective: An implicit assumption in the use of depressive severity measures to assess change during treatment, such as the Hamilton Rating Scale for Depression (HRSD), is that reductions from pre- to post-treatment that are equal to each other are of equal value. However, stakeholders' valuations of changes might depart substantially from this assumption. Method: Vignettes were constructed that reflected the six possible 1, 2, and 3-point reductions on five cognitive and four somatic symptoms derived from the HRSD. Former or currently depressed patients provided judgments of the importance of the symptom reductions. Mean importance ratings were modeled using symptom category and the pre/post-treatment combination. Differences were explored using the Tukey method. Results: Results indicated that mean ratings, from most to least important, were: Anxiety, Suicide, Depressed Mood, Work, and Guilt (the cognitive symptoms) followed by Somatic, Sleep, Appetite & Weight, and Retardation (the somatic symptoms). Participants valued reductions that resulted in posttreatment scores of zero more than expected, given the magnitude of the reductions. Conclusions: The value of reductions in symptoms captured by the HRSD, as judged by patients, appears to differ as a function of symptom category and the post-treatment score. Similar patterns might characterize other measures of depression severity. [ABSTRACT FROM AUTHOR]

Objective: We use a new variable selection procedure for treatment selection which generates treatment recommendations based on pre-treatment characteristics for adults with mild-to-moderate depression deciding between cognitive behavioral (CBT) versus psychodynamic therapy (PDT). Method: Data are drawn from a randomized comparison of CBT versus PDT for depression (N = 167, 71% female, mean-age = 39.6). The approach combines four different statistical techniques to identify patient characteristics associated consistently with differential treatment response. Variables are combined to generate predictions indicating each individual's optimal-treatment. The average outcomes for patients who received their indicated treatment versus those who did not were compared retrospectively to estimate model utility. Results: Of 49 predictors examined, depression severity, anxiety sensitivity, extraversion, and psychological treatment-needs were included in the final model. The average post-treatment Hamilton-Depression-Rating-Scale score was 1.6 points lower (95%CI = [0.5:2.8]; d = 0.21) for those who received their indicated-treatment compared to non-indicated. Among the 60% of patients with the strongest treatment recommendations, that advantage grew to 2.6 (95%CI = [1.4:3.7]; d = 0.37). Conclusions: Variable selection procedures differ in their characterization of the importance of predictive variables. Attending to consistently-indicated predictors may be sensible when constructing treatment selection models. The small N and lack of separate validation sample indicate a need for prospective tests before this model is used. [ABSTRACT FROM AUTHOR]