Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAe, Centre for Research in Epidemiology and Statistics (CRESS), Hôpital Hôtel Dieu, 1 Place du Parvis Notre-Dame, 75004, Paris, France.
Department of Anesthesiology and Critical Care, AP-HP, Cochin Hospital, 75004, Paris, France.
Syst Rev. 2024 Feb 21;13(1):70. doi: 10.1186/s13643-024-02464-w.
This study examined the synthesis methods used in meta-analyses pooling data from observational studies (OSs) and randomised controlled trials (RCTs) from various medical disciplines.
We searched Medline via PubMed to identify reports of systematic reviews of interventions, including and pooling data from RCTs and OSs published in 110 high-impact factor general and specialised journals between 2015 and 2019. Screening and data extraction were performed in duplicate. To describe the synthesis methods used in the meta-analyses, we considered the first meta-analysis presented in each article.
Overall, 132 reports were identified with a median number of included studies of 14 [9-26]. The median number of OSs was 6.5 [3-12] and that of RCTs was 3 [1-6]. The effect estimates recorded from OSs (i.e., adjusted or unadjusted) were not specified in 82% (n = 108) of the meta-analyses. An inverse-variance common-effect model was used in 2% (n = 3) of the meta-analyses, a random-effects model was used in 55% (n = 73), and both models were used in 40% (n = 53). A Poisson regression model was used in 1 meta-analysis, and 2 meta-analyses did not report the model they used. The mean total weight of OSs in the studied meta-analyses was 57.3% (standard deviation, ± 30.3%). Only 44 (33%) meta-analyses reported results stratified by study design. Of them, the results between OSs and RCTs had a consistent direction of effect in 70% (n = 31). Study design was explored as a potential source of heterogeneity in 79% of the meta-analyses, and confounding factors were investigated in only 10% (n = 13). Publication bias was assessed in 70% (n = 92) of the meta-analyses. Tau-square was reported in 32 meta-analyses with a median of 0.07 [0-0.30].
The inclusion of OSs in a meta-analysis on interventions could provide useful information. However, considerations of several methodological and conceptual aspects of OSs, that are required to avoid misleading findings, were often absent or insufficiently reported in our sample.
本研究考察了将来自不同医学学科的观察性研究(OS)和随机对照试验(RCT)的数据进行荟萃分析时使用的综合方法。
我们通过 PubMed 中的 Medline 搜索了 2015 年至 2019 年期间发表在 110 种高影响力的普通和专业期刊中关于干预措施的系统评价报告,这些报告包括并汇总了 RCT 和 OS 的数据。筛选和数据提取均由两人进行。为了描述荟萃分析中使用的综合方法,我们考虑了每篇文章中呈现的第一个荟萃分析。
总共确定了 132 篇报告,纳入研究的中位数为 14 项[9-26]。OS 的中位数为 6.5 项[3-12],RCT 的中位数为 3 项[1-6]。在 82%(n=108)的荟萃分析中未明确记录来自 OS 的效应估计值(即调整或未调整)。2%(n=3)的荟萃分析使用了逆方差固定效应模型,55%(n=73)使用了随机效应模型,40%(n=53)同时使用了这两种模型。1 项荟萃分析使用泊松回归模型,2 项荟萃分析未报告其使用的模型。在研究中的荟萃分析中,OS 的总权重平均值为 57.3%(标准差,±30.3%)。只有 44 项(33%)荟萃分析报告了按研究设计分层的结果。其中,在 70%(n=31)的情况下,OS 和 RCT 的结果具有一致的效应方向。在 79%的荟萃分析中,研究设计被探索为异质性的潜在来源,而仅在 10%(n=13)的荟萃分析中调查了混杂因素。70%(n=92)的荟萃分析评估了发表偏倚。32 项荟萃分析报告了 Tau-square,中位数为 0.07[0-0.30]。
荟萃分析纳入 OS 可以提供有用的信息。然而,在我们的样本中,经常缺乏或未充分报告考虑 OS 的几个方法学和概念方面,这些方面对于避免误导性发现是必要的。