结合 Huber M 估计的基于矩的随机效应 Meta 分析
Moment-based Random-effects Meta-analysis Equipped with Huber's M-Estimation
AI总结：
本文将 Huber M 估计融入基于矩的随机效应 Meta 分析，以改善小样本下研究间方差低估问题，并通过联合分布可视化提升治疗效应推断与置信区间准确性。
摘要：
Meta 分析通常用于在众多研究之间提供可靠证据。尽管已有更准确的替代方法，但 DerSimonian–Laird 方法等传统矩方法仍然很常用。虽然矩估计量简单直观，但众所周知，它们会低估总体治疗效应的方差，尤其是在研究数量较少时。这种低估会导致置信区间过窄，无法达到名义置信水平，并可能产生误导性结论。在本研究中，我们通过引入 Huber 的 M 估计来改进传统的基于矩的 Meta 分析方法，以更准确地捕捉研究间方差的分布特征。即使在研究数量较少、几乎所有现有方法都会低估研究间方差的情况下，我们的方法仍能够实现保守的参数估计。此外，通过推导总体治疗效应与研究间方差的联合分布，我们提出促进对这两个量之间关系进行可视化探索。我们的方法为总体治疗效应和研究间方差提供了更可靠的估计量，尤其适用于研究较少的情形。通过模拟和真实数据分析，我们证明，与传统矩方法相比，该方法始终给出更保守的结果，并确保 Meta 分析中的置信区间更加准确。
英文摘要：
Meta-analyses are commonly used to provide solid evidence across numerous studies. Traditional moment methods, such as the DerSimonian-Laird method, remain popular in spite of the availability of more accurate alternatives. While moment estimators are simple and intuitive, they are known to underestimate the variance of the overall treatment effect, particularly when the number of studies is small. This underestimation can lead to excessively narrow confidence intervals that do not meet the nominal confidence level, potentially resulting in misleading conclusions. In this study, we improve traditional moment-based meta-analysis methods by incorporating Huber's M-estimation to more accurately capture the distributional characteristics of between-study variance. Our approach enables conservative parameter estimation, even when almost all existing methods lead to underestimation of between-study variance under a small number of studies. Additionally, by deriving the simultaneous distribution of overall treatment effect and between-study variance, we propose facilitating a visual exploration of the relationship between these two quantities. Our method provides more reliable estimators for the overall treatment effect and between-study variance, particularly in situations with few studies. Using simulations and real data analysis, we demonstrate that our approach always yields more conservative results compared to traditional moment methods, and ensures more accurate confidence intervals in meta-analyses.
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