发表机构
Beijing University of Technology; Yunnan University of Finance and Economics; Renmin University of China; Hong Kong Baptist University（北京工业大学; 云南财经大学; 中国人民大学; 香港浸会大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对二分类结局Meta分析，提出基于潜在连续变量模型的样本量无关异质性度量ICC_MA^OR及其估计量I^2_A，模拟和实际数据验证其无偏且与连续结局度量一致。
AI 中文摘要
量化异质性是Meta分析中的一个重要问题。传统的异质性统计量$I^2$严重依赖于研究样本量，并随着样本量的增加而趋于接近1。为克服这一局限性，近期开发了一种与样本量无关的度量${\ m ICC}_{\ m MA}$来量化异质性，但该方法仅限于连续结局的Meta分析。然而在实践中，二分类结局更为常见，其中对数比值比（lnOR）常被用作效应量，以比值形式比较两种治疗的效果。为填补这一空白，我们通过引入潜在连续变量模型，提出了一种适用于二分类结局Meta分析的新异质性度量。在该框架下，所提出的度量在对数比值比作为效应量的随机效应Meta分析模型中具有闭式表达式。我们将这一与样本量无关的度量定义为${\ m ICC}_{\ m MA}^{\ m OR}$，并进一步提出一个新估计量$I^2_A$来评估该度量。模拟研究表明，我们的新估计量几乎无偏，并且在连续变量可观测时，与其应用于标准化均数差的对应估计量高度一致。一项实际数据应用进一步表明，所提出的针对二分类结局的异质性统计量及其针对连续结局的对应统计量，在不同结局类型间提供了可解释且一致的估计。
英文摘要
Quantifying the heterogeneity is an important issue in meta-analysis. The conventional heterogeneity statistic, $I^2$, heavily depends on study sample sizes and tends to approach one as the sample sizes increase. To overcome this limitation, a sample size independent measure, ${\rm ICC}_{\rm MA}$, has recently been developed to quantify the heterogeneity, yet this method is restricted to meta-analysis with continuous outcomes. In practice, however, binary outcomes are more widely encountered, with the log odds ratio (lnOR) frequently adopted as an effect size to compare the effects of two treatments in terms of odds. To fill the gap, we propose a new heterogeneity measure for meta-analysis with binary outcomes by introducing a latent continuous variable model. Under this framework, the proposed measure admits a closed-form expression within the random-effects meta-analysis model with lnOR as the effect size. We define this sample size independent measure as ${\rm ICC}_{\rm MA}^{\rm OR}$, and moreover propose a new estimator, $I^2_A$, to evaluate this measure. Simulation studies demonstrate that our new estimator is nearly unbiased and aligns closely with its counterpart applied for standardized mean differences when the continuous variable is observable. A real data application further demonstrates that the proposed heterogeneity statistic for binary outcomes and its counterpart for continuous outcomes provide interpretable and coherent estimates across different outcome types.
Comments32 pages including appendices, 2 tables, 7 figures (5 in the main text and 2 in the appendices)
