Background Hundreds of randomised controlled trials and dozens of meta-analyses have examined psychotherapies for depression—yet not all points in the same direction. Are these discrepancies a result of specific meta-analytical decisions or do most analytical strategies reaching the same conclusion? Objective We aim to solve these discrepancies by conducting a multiverse meta-analysis containing all possible meta-analyses, using all statistical methods. Study selection and analysis We searched four bibliographical databases (PubMed, EMBASE, PsycINFO and Cochrane Register of Controlled Trials), including studies published until 1 January 2022. We included all randomised controlled trials comparing psychotherapies with control conditions without restricting the type of psychotherapy, target group, intervention format, control condition and diagnosis. We defined all possible meta-analyses emerging from combinations of these inclusion criteria and estimated the resulting pooled effect sizes with fixed-effect, random-effects, 3-level, robust variance estimation, p -uniform and PET-PEESE (precision-effect test and precision-effect estimate with SE) meta-analysis models. This study was preregistered (). Findings A total of 21 563 records were screened, and 3584 full texts were retrieved; 415 studies met our inclusion criteria containing 1206 effect sizes and 71 454 participants. Based on all possible combinations between inclusion criteria and meta-analytical methods, we calculated 4281 meta-analyses. The average summary effect size for these meta-analyses was Hedges’ g mean=0.56, a medium effect size, and ranged from g =−0.66 to 2.51. In total, 90% of these meta-analyses reached a clinically relevant magnitude. Conclusions and Clinical Implications The multiverse meta-analysis revealed the overall robustness of the effectiveness of psychotherapies for depression. Notably, meta-analyses that included studies with a high risk of bias, compared the intervention with wait-list control groups, and not correcting for publication bias produced larger effect sizes. Data are available in a public, open access repository. The R code and data to reproduce all analyses can be found at the Open Science Framework.

中文翻译：

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探索心理疗法对抑郁症的疗效：多元宇宙荟萃分析

背景数百项随机对照试验和数十项荟萃分析已经检验了抑郁症的心理疗法，但并非所有观点都朝着同一方向。这些差异是特定荟萃分析决策的结果还是大多数分析策略得出相同的结论？目的 我们的目标是通过使用所有统计方法进行包含所有可能的荟萃分析的多元宇宙荟萃分析来解决这些差异。研究选择和分析 我们检索了四个书目数据库（PubMed、EMBASE、PsycINFO 和 Cochrane Register of Controlled Trials），包括截至 2022 年 1 月 1 日发表的研究。我们纳入了所有比较心理治疗与对照条件的随机对照试验，不限制心理治疗的类型、目标分组、干预形式、控制条件和诊断。我们定义了这些纳入标准组合中出现的所有可能的荟萃分析，并使用固定效应、随机效应、三水平、稳健方差估计、p-uniform 和 PET-PEESE（精确效应检验）估计了所得的汇总效应大小。以及使用 SE）荟萃分析模型进行精确效果估计。本研究已预先注册（）。结果 共筛选记录21 563条，检索全文3584篇；415 项研究符合我们的纳入标准，其中包含 1206 项效应量和 71 454 名参与者。基于纳入标准和荟萃分析方法之间的所有可能组合，我们计算了 4281 项荟萃分析。这些荟萃分析的平均汇总效应大小为 Hedges 的 g 平均值 = 0.56，中等效应大小，范围从 g = -0.66 到 2.51。总的来说，90% 的荟萃分析达到了临床相关程度。结论和临床意义多元荟萃分析揭示了心理疗法治疗抑郁症的总体稳健性。值得注意的是，荟萃分析包括具有高偏倚风险的研究，将干预措施与候补名单对照组进行比较，并且不纠正发表偏倚，产生了更大的效应量。数据可在公共、开放访问存储库中获取。用于重现所有分析的 R 代码和数据可以在开放科学框架中找到。