Most research on the dose-effect model of change has combined data across patients who vary in their total dose of treatment and has implicitly assumed that the rate of change during therapy is constant across doses. In contrast, the good-enough level model predicts that rate of change will be related to total dose of therapy. In this study, the authors evaluated these competing predictions by examining the relationship between rate of change and total dose in 4,676 psychotherapy patients who received individual psychotherapy. Patients attended 6.46 sessions on average (SD = 4.14, range = 3-29, Mdn = 5). The results indicated that although patients improved during treatment, patients' rate of change varied as a function of total dose of treatment. Small doses of treatment were related to relatively fast rates of change, whereas large doses of treatment were related to slower rates of change. Total dose had a nonlinear relationship with the likelihood of clinically significant change. Given the variability in rates of change, it appears that time limits for treatment uniform to all patients would not adequately serve patients' needs.

This meta-analysis summarizes results from k = 24 studies comparing either Brief Strategic Family Therapy, Functional Family Therapy, Multidimensional Family Therapy, or Multisystemic Therapy to either treatment-as-usual, an alternative therapy, or a control group in the treatment of adolescent substance abuse and delinquency. Additionally, the authors reviewed and applied three advanced meta-analysis methods including influence analysis, multivariate meta-analysis, and publication bias analyses. The results suggested that as a group the four family therapies had statistically significant, but modest effects as compared to treatment-as-usual (d = 0.21; k = 11) and as compared to alternative therapies (d = 0.26; k = 11). The effect of family therapy compared to control was larger (d = 0.70; k = 4) but was not statistically significant probably because of low power. There was insufficient evidence to determine whether the various models differed in their effectiveness relative to each other. Influence analyses suggested that three studies had a large effect on aggregate effect sizes and heterogeneity statistics. Moderator and multivariate analyses were largely underpowered but will be useful as this literature grows.

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Research on the effects of progress feedback and clinician problem-solving tools on patient outcome has been limited to a few clinical problems and settings (Shimokawa, Lambert, & Smart, 2010). Although these interventions work well in outpatient settings their effects so far have not been investigated with eating-disordered patients or in inpatient care. In this study, the effect of providing feedback interventions was investigated in a randomized clinical trial involving 133 females diagnosed with anorexia nervosa, bulimia nervosa, or eating disorders not otherwise specified. Comparisons were made between the outcomes of patients randomly assigned to either treatment-as-usual (TAU) or an experimental condition (Fb) within therapists (the same therapists provided both treatments). Patients in the Fb condition more frequently experienced clinically significant change than those who had TAU (52.95% vs. 28.6%). Similar trends were noted within diagnostic groups. In terms of pre to post change in mental health functioning, large effect sizes favored Fb over TAU. Patients' BMI improved substantially in both TAU and the feedback condition. The effects of feedback were consistent with past research on these approaches although the effect size was smaller in this study. Suggestions for further research are delineated.

Meta-analysis consists of a set of statistical techniques for quantitatively aggregating and summarizing the results of several studies. This article provides an introductory tutorial to the process of meta-analysis for psychotherapy outcome research, including identifying and collecting studies, coding effect sizes, coding substantive and methodological information, combining effect sizes, interpreting effect sizes, and conducting moderator and sensitivity analyses. To assist readers new to meta-analysis, the parallels between the process of primary research and the process of meta-analysis are highlighted. Software for conducting meta-analytic research is briefly discussed.