Iaquinto Stefania, Bührer Lea, Feldmann Maria, Latal Beatrice, Held Ulrike
Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Centre for Computational Health, Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW), Wädenswil, Switzerland.
Syst Rev. 2025 Jul 5;14(1):138. doi: 10.1186/s13643-025-02831-1.
Random-effects meta-analysis models account for between-study heterogeneity by estimating and incorporating the heterogeneity variance parameter . Numerous estimators for have been proposed, but no widely accepted guidance exists on when to best use which heterogeneity variance estimator. Especially in the context of single-arm observational studies, studies with unique challenges, such as outcome measure variability, sparse data, and high methodological heterogeneity, systematic evaluations and comparisons of the various heterogeneity variance estimators are lacking. This study investigates the advantages of different heterogeneity variance estimators for typical single-arm meta-analysis scenarios through comprehensive simulations in a neutral comparison study setting and with an empirical application in pediatrics.
We compared seven heterogeneity variance estimators for random-effects meta-analysis. The estimators were selected on the basis of methodological diversity and availability and were evaluated both empirically and in a simulation study. We simulated typical meta-analysis scenarios for continuous and binary outcomes in a single-arm meta-analysis setting. Through a non-systematic literature review, we assessed which heterogeneity variance estimators are currently used in high-ranked journals, and evaluated their reporting quality.
Our simulation study showed that all evaluated heterogeneity estimators were imprecise and often failed to estimate the true amount of heterogeneity. The estimation is particularly imprecise in situations where the meta-analysis contained few studies or when the binary outcomes included rare events. Moreover, we discovered that most heterogeneity variance estimators produce zero heterogeneity estimates under all simulated conditions, even though heterogeneity was present. The estimated overall effect was found to be relatively robust to different estimators in the empirical application and in our simulation study. However, the prediction intervals for the overall effect vary depending on the estimator chosen.
Although different heterogeneity variance estimators produce substantially different heterogeneity variance estimates, too little attention is paid to selecting a suitable heterogeneity variance estimator in single-arm evidence synthesis. Based on our literature review, we conclude that the awareness of different heterogeneity variance estimators and their properties needs to be strengthened in practice. Given that it is rarely appropriate to rely on a single heterogeneity variance estimator, we suggest careful consideration and evaluation of a wider range of plausible estimators in a sensitivity analysis before drawing a final conclusion about the meta-analysis results.
随机效应荟萃分析模型通过估计并纳入异质性方差参数来考虑研究间的异质性。针对该参数已提出众多估计方法，但对于何时最佳使用哪种异质性方差估计方法，尚无广泛认可的指导意见。特别是在单臂观察性研究的背景下，这类研究存在诸多独特挑战，如结局指标变异性、数据稀疏以及方法学异质性高，目前缺乏对各种异质性方差估计方法的系统评估和比较。本研究通过在中立的比较研究环境中进行全面模拟以及在儿科领域的实证应用，探讨不同异质性方差估计方法在典型单臂荟萃分析场景中的优势。
我们比较了随机效应荟萃分析的七种异质性方差估计方法。这些估计方法基于方法学多样性和可得性进行选择，并在实证研究和模拟研究中进行评估。我们在单臂荟萃分析环境中模拟了连续和二元结局的典型荟萃分析场景。通过非系统性文献综述，我们评估了高排名期刊目前使用的异质性方差估计方法，并评估了它们的报告质量。
我们的模拟研究表明，所有评估的异质性估计方法都不准确，并且常常无法估计出真正的异质性程度。在荟萃分析包含的研究较少或二元结局包含罕见事件的情况下，估计尤其不准确。此外，我们发现，即使存在异质性，大多数异质性方差估计方法在所有模拟条件下都得出零异质性估计值。在实证应用和我们的模拟研究中，发现估计的总体效应对于不同的估计方法相对稳健。然而，总体效应的预测区间因所选估计方法而异。
尽管不同的异质性方差估计方法会产生截然不同的异质性方差估计值，但在单臂证据合成中，对于选择合适的异质性方差估计方法关注甚少。基于我们的文献综述，我们得出结论，在实践中需要加强对不同异质性方差估计方法及其特性的认识。鉴于仅依赖单一异质性方差估计方法很少合适，我们建议在对荟萃分析结果得出最终结论之前，在敏感性分析中仔细考虑和评估更广泛的合理估计方法。
