Meta‐analyses based on systematic literature reviews are commonly used to obtain a quantitative summary of the available evidence on a given topic. However, the reliability of any meta‐analysis is constrained by that of its constituent studies. One major limitation is the possibility of small‐study effects, when estimates from smaller and larger studies differ systematically. Small‐study effects may result from reporting biases (ie, publication bias), from inadequacies of the included studies that are related to study size, or from reasons unrelated to bias. We propose two estimators based on the median and mode to increase the reliability of findings in a meta‐analysis by mitigating the influence of small‐study effects. By re‐examining data from published meta‐analyses and by conducting a simulation study, we show that these estimators offer robustness to a range of plausible bias mechanisms, without making explicit modelling assumptions. They are also robust to outlying studies without explicitly removing such studies from the analysis. When meta‐analyses are suspected to be at risk of bias because of small‐study effects, we recommend reporting the mean, median and modal pooled estimates.

中文翻译：

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在存在小研究效应和离群值的情况下，中位数和众数作为稳健的荟萃分析估计量。

基于系统文献综述的荟萃分析通常用于获得有关给定主题的可用证据的定量摘要。但是，任何荟萃分析的可靠性都受到其组成研究的约束。一个主要的局限性是当小规模研究和大型研究的估计值在系统上有所不同时，可能会产生小规模研究效应。小型研究的影响可能是由于报告的偏倚（即出版偏倚），与研究规模有关的纳入研究不足或与偏倚无关的原因所致。我们建议基于中位数和众数的两个估计量，以通过减轻小规模研究效应的影响来提高荟萃分析结果的可靠性。通过重新检查已发布的荟萃分析中的数据并进行模拟研究，我们表明，这些估计量为一系列可能的偏差机制提供了鲁棒性，而没有做出明确的建模假设。它们对于外围研究也很健壮，而无需从分析中明确删除此类研究。当由于小研究影响而怀疑荟萃分析有偏见的风险时，我们建议报告均值，中位数和模态合并估计值。