The common-effect model and the random-effects model are the two most popular models for meta-analysis in the literature. To choose a proper model between them, the Q statistic and the I statistic are commonly used as the criteria. Recently, it is recognized that the fixed-effects model is also essential for meta-analysis, especially when the number of studies is small. With this new model, the existing methods are no longer sufficient for model selection in metaanalysis. In view of the demand, we propose a novel method for model selection between the fixed-effects model and the random-effects model. Specifically, we apply the Akaike information criterion (AIC) to both models and then select the model with a smaller AIC value. A real data example is also presented to illustrate how the new method can be applied. We further propose the generalized AIC (GAIC) to reduce the large variation in the AIC value, and demonstrate its superiority through real data analysis and simulation studies. To the best of our knowledge, this is the first work in meta-analysis for model selection between the fixed-effects model and the random-effects model, and we expect that our new criterion has the potential to be widely applied in meta-analysis and evidence-based medicine.

中文翻译：

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Meta分析中固定效应模型与随机效应模型的模型选择

共同效应模型和随机效应模型是文献中最流行的两种荟萃分析模型。为了在它们之间选择合适的模型，通常使用 Q 统计量和 I 统计量作为标准。最近，人们认识到固定效应模型对于荟萃分析也是必不可少的，尤其是在研究数量较少的情况下。有了这个新模型，现有的方法不再足以用于荟萃分析中的模型选择。鉴于需求，我们提出了一种在固定效应模型和随机效应模型之间进行模型选择的新方法。具体来说，我们将 Akaike 信息准则 (AIC) 应用于两个模型，然后选择 AIC 值较小的模型。还提供了一个真实的数据示例来说明如何应用新方法。我们进一步提出了广义 AIC（GAIC）来减少 AIC 值的大变化，并通过真实数据分析和模拟研究证明其优越性。据我们所知，这是固定效应模型和随机效应模型之间模型选择的荟萃分析的第一项工作，我们预计我们的新标准有可能在荟萃分析中得到广泛应用和循证医学。