The Contribution of Individual Participant Data Meta-Analyses of Psychotherapies for Depression to the Development of Personalized Treatments: A Systematic Review

While randomized trials typically lack sufficient statistical power to identify predictors and moderators of outcome, individual participant data (IPD) meta-analyses, which combine primary data of multiple randomized trials, can increase the statistical power to identify predictors and moderators of outcome. We conducted a systematic review of IPD meta-analyses on psychological treatments of depression to provide an overview of predictors and moderators identified. We included 10 (eight pairwise and two network) IPD meta-analyses. Six meta-analyses showed that higher baseline depression severity was associated with better outcomes, and two found that older age was associated with better outcomes. Because power was high in most IPD meta-analyses, non-significant findings are also of interest because they indicate that these variables are probably not relevant as predictors and moderators. We did not find in any IPD meta-analysis that gender, education level, or relationship status were significant predictors or moderators. This review shows that IPD meta-analyses on psychological treatments can identify predictors and moderators of treatment effects and thereby contribute considerably to the development of personalized treatments of depression.

Automated summary

Individual participant data (IPD) meta-analyses can provide sufficient statistical power to identify predictors and moderators of psychological treatments for depression. The analysis showed that higher depression severity and older age were associated with better treatment outcomes, while gender, education level, and relationship status were not significant predictors or moderators.