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利用临床试验注册库为 Copas 选择模型提供信息,以评估荟萃分析中的发表偏倚。

Using clinical trial registries to inform Copas selection model for publication bias in meta-analysis.

机构信息

Department of Biomedical Statistics, Graduate School of Medicine, Osaka University, Osaka, Japan.

Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

出版信息

Res Synth Methods. 2021 Sep;12(5):658-673. doi: 10.1002/jrsm.1506. Epub 2021 Jul 4.

Abstract

Prospective registration of study protocols in clinical trial registries is a useful way to minimize the risk of publication bias in meta-analysis, and several clinical trial registries are available nowadays. However, they are mainly used as a tool for searching studies and information submitted to the registries has not been utilized as efficiently as it could. In addressing publication bias in meta-analyses, sensitivity analysis with the Copas selection model is a more objective alternative to widely-used graphical methods such as the funnel-plot and the trim-and-fill method. Despite its ability to quantify the potential impact of publication bias, the Copas selection model relies on sensitivity analyses, in which some parameters are varied across a certain range. This may result in some difficulty in interpreting the results. In this paper, we propose an alternative inference procedure for the Copas selection model by utilizing information from clinical trial registries. Our method provides a simple and accurate way to estimate all unknown parameters of the Copas selection model. A simulation study revealed that our proposed method resulted in smaller biases and more accurate confidence intervals than existing methods. Furthermore, three published meta-analyses were re-analyzed to demonstrate how to implement the proposed method in practice.

摘要

前瞻性地在临床试验注册库中注册研究方案是一种减少荟萃分析中发表偏倚风险的有效方法,目前有几个临床试验注册库可用。然而,它们主要被用作搜索研究的工具,而注册库中提交的信息并没有得到充分利用。在解决荟萃分析中的发表偏倚问题时,Copas 选择模型的敏感性分析是对漏斗图和填充法等广泛使用的图形方法的更客观替代方法。尽管 Copas 选择模型能够量化发表偏倚的潜在影响,但它依赖于敏感性分析,其中一些参数在一定范围内变化。这可能会导致解释结果有些困难。在本文中,我们通过利用临床试验注册库中的信息,提出了 Copas 选择模型的替代推断程序。我们的方法为 Copas 选择模型的所有未知参数提供了一种简单而准确的估计方法。模拟研究表明,与现有方法相比,我们提出的方法导致的偏差更小,置信区间更准确。此外,还重新分析了三个已发表的荟萃分析,以演示如何在实践中实施所提出的方法。

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