472: Progestogen in twin pregnancies: an individual participant data meta-analysis of randomized trials

2012 ◽  
Vol 206 (1) ◽  
pp. S215-S216 ◽  
Author(s):  
E. Schuit ◽  
Sarah Stock ◽  
Dwight Rouse ◽  
A.C. Lim ◽  
Line Rode ◽  
...  
Keyword(s):  
Randomized Trials ◽  
Meta Analysis ◽  
Individual Participant Data ◽  
Individual Participant ◽  
Twin Pregnancies

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

2022 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Pim Cuijpers ◽  
Marketa Ciharova ◽  
Soledad Quero ◽  
Clara Miguel ◽  
Ellen Driessen ◽  
...  
Keyword(s):  
Systematic Review ◽  
Statistical Power ◽  
Randomized Trials ◽  
Meta Analysis ◽  
Primary Data ◽  
Individual Participant Data ◽  
Psychological Treatments ◽  
Gender Education ◽  
Individual Participant ◽  
Meta Analyses

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.

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