神经影像学研究中广义可加模型的荟萃分析
Meta-Analysis of Generalized Additive Models in Neuroimaging Studies
摘要：
分析多项神经影像学研究的数据在增加统计功效方面具有巨大潜力，能够检测到比单独分析每项研究时可能检测到的更小幅度的效应，并且还能够系统地研究研究间差异。由于隐私或专有数据的限制以及更多的实际问题可能使共享神经成像数据集变得困难，因此在一个公共位置分析所有数据可能是不切实际或不可能的。元分析方法提供了一种克服这一问题的方法，通过结合模型参数或风险比等聚合量。大多数元分析工具集中于参数统计模型，而用于元分析半参数模型（如广义可加模型）的方法尚未得到很好的发展。参数模型通常不适用于神经成像，例如年龄-大脑关系可能采用难以使用此类模型准确描述的形式。在本文中，我们介绍元GAM，荟萃分析的广义可加模型，不需要个人参与者的数据，因此是适合于增加统计功效，同时维护隐私和其他监管问题的方法。我们扩展了以前的工作，使多个模型项的分析，以及多元平滑函数。此外，我们展示了如何元分析$p$ -值可以计算光滑项。所提出的方法在模拟实验中表现良好，并证明了在一个真实的数据分析海马体积和自我报告的睡眠质量数据从Lifebrain财团。我们认为，元GAM的应用是特别有益的寿命神经科学和成像遗传学。这些方法在附带的R包\verb！metagam！，这一点也得到了证实。
英文摘要：
Analyzing data from multiple neuroimaging studies has great potential in terms of increasing statistical power, enabling detection of effects of smaller magnitude than would be possible when analyzing each study separately and also allowing to systematically investigate between-study differences. Restrictions due to privacy or proprietary data as well as more practical concerns can make it hard to share neuroimaging datasets, such that analyzing all data in a common location might be impractical or impossible. Meta-analytic methods provide a way to overcome this issue, by combining aggregated quantities like model parameters or risk ratios. Most meta-analytic tools focus on parametric statistical models, and methods for meta-analyzing semi-parametric models like generalized additive models have not been well developed. Parametric models are often not appropriate in neuroimaging, where for instance age-brain relationships may take forms that are difficult to accurately describe using such models. In this paper we introduce meta-GAM, a method for meta-analysis of generalized additive models which does not require individual participant data, and hence is suitable for increasing statistical power while upholding privacy and other regulatory concerns. We extend previous works by enabling the analysis of multiple model terms as well as multivariate smooth functions. In addition, we show how meta-analytic $p$-values can be computed for smooth terms. The proposed methods are shown to perform well in simulation experiments, and are demonstrated in a real data analysis on hippocampal volume and self-reported sleep quality data from the Lifebrain consortium. We argue that application of meta-GAM is especially beneficial in lifespan neuroscience and imaging genetics. The methods are implemented in an accompanying R package \verb!metagam!, which is also demonstrated.
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