Using selection models to assess sensitivity to publication bias: A tutorial and call for more routine use.
Using selection models to assess sensitivity to publication bias: A tutorial and call for more routine use.
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DOI：
10.1002/cl2.1256
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发表时间：
2022-09
影响因子：
3.2
通讯作者：
中科院分区：
文献类型：
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在Meta分析中，评估发表偏倚可能损害结果的程度至关重要。基于漏斗图的经典方法，包括Egger检验和Trim‐and‐Fill，已成为事实上的默认方法，最近顶级医学期刊（85%）中的大多数Meta分析仅使用这些方法评估发表偏倚。然而，这些经典的漏斗图方法在用作评估发表偏倚的唯一方法时具有重要的局限性：它们基本上假设发表过程有利于小型研究的大点估计，并且不影响最大的研究，并且当效应异质时，它们可能表现不佳。鉴于这些局限性，我们建议Meta分析常规使用其他发表偏倚方法，作为经典漏斗图方法的补充或替代。为此，我们描述了如何使用和解释选择模型。这些方法通常更现实地假设发表偏倚有利于“统计学显著”的结果，并且这些方法还直接适应效应异质性。选择模型在统计学文献中已经建立了几十年，并得到了用户友好软件的支持，但在许多学科中仍然很少报道。我们使用先前发表的Meta分析来证明选择模型可以产生超越漏斗图方法提供的见解，这表明建立更全面的报告实践对发表偏倚评估的重要性。
In meta‐analyses, it is critical to assess the extent to which publication bias might have compromised the results. Classical methods based on the funnel plot, including Egger's test and Trim‐and‐Fill, have become the de facto default methods to do so, with a large majority of recent meta‐analyses in top medical journals (85%) assessing for publication bias exclusively using these methods. However, these classical funnel plot methods have important limitations when used as the sole means of assessing publication bias: they essentially assume that the publication process favors large point estimates for small studies and does not affect the largest studies, and they can perform poorly when effects are heterogeneous. In light of these limitations, we recommend that meta‐analyses routinely apply other publication bias methods in addition to or instead of classical funnel plot methods. To this end, we describe how to use and interpret selection models. These methods make the often more realistic assumption that publication bias favors “statistically significant” results, and the methods also directly accommodate effect heterogeneity. Selection models have been established for decades in the statistics literature and are supported by user‐friendly software, yet remain rarely reported in many disciplines. We use a previously published meta‐analysis to demonstrate that selection models can yield insights that extend beyond those provided by funnel plot methods, suggesting the importance of establishing more comprehensive reporting practices for publication bias assessment.