Zhou Xingcai, Yeon Miyeon, Wang Jiangyan, Ding Shengxian, Lei Kaizhou, Zhao Yanyong, Liu Rongjie, Huang Chao
Institute of Statistics and Data Science, Nanjing Audit University, Nanjing, China.
Department of Statistics, Florida State University, Tallahassee, FL, USA.
Stat Med. 2024 Dec 30;43(30):5698-5710. doi: 10.1002/sim.10265. Epub 2024 Nov 12.
As a crucial tool in neuroscience, mediation analysis has been developed and widely adopted to elucidate the role of intermediary variables derived from neuroimaging data. Typically, structural equation models (SEMs) are employed to investigate the influences of exposures on outcomes, with model coefficients being interpreted as causal effects. While existing SEMs have proven to be effective tools for mediation analysis involving various neuroimaging-related mediators, limited research has explored scenarios where these mediators are derived from the shape space. In addition, the linear relationship assumption adopted in existing SEMs may lead to substantial efficiency losses and decreased predictive accuracy in real-world applications. To address these challenges, we introduce a novel framework for shape mediation analysis, designed to explore the causal relationships between genetic exposures and clinical outcomes, whether mediated or unmediated by shape-related factors while accounting for potential confounding variables. Within our framework, we apply the square-root velocity function to extract elastic shape representations, which reside within the linear Hilbert space of square-integrable functions. Subsequently, we introduce a two-layer shape regression model to characterize the relationships among neurocognitive outcomes, elastic shape mediators, genetic exposures, and clinical confounders. Both estimation and inference procedures are established for unknown parameters along with the corresponding causal estimands. The asymptotic properties of estimated quantities are investigated as well. Both simulated studies and real-data analyses demonstrate the superior performance of our proposed method in terms of estimation accuracy and robustness when compared to existing approaches for estimating causal estimands.
作为神经科学中的一项关键工具，中介分析已得到发展并被广泛应用，以阐明源自神经影像数据的中介变量的作用。通常，结构方程模型（SEMs）用于研究暴露因素对结果的影响，模型系数被解释为因果效应。虽然现有的结构方程模型已被证明是涉及各种与神经影像相关的中介变量的中介分析的有效工具，但对于这些中介变量源自形状空间的情况，相关研究较少。此外，现有结构方程模型中采用的线性关系假设在实际应用中可能导致效率大幅损失和预测准确性下降。为应对这些挑战，我们引入了一种新颖的形状中介分析框架，旨在探索基因暴露与临床结果之间的因果关系，无论是否由形状相关因素介导，并同时考虑潜在的混杂变量。在我们的框架内，我们应用平方根速度函数来提取弹性形状表示，这些表示位于平方可积函数的线性希尔伯特空间内。随后，我们引入了一个两层形状回归模型来描述神经认知结果、弹性形状中介变量、基因暴露和临床混杂因素之间的关系。针对未知参数以及相应的因果估计量建立了估计和推断程序。还研究了估计量的渐近性质。模拟研究和实际数据分析均表明，与现有估计因果估计量的方法相比，我们提出的方法在估计准确性和稳健性方面具有优越的性能。
