A Bayesian joint model for mediation analysis with matrix-valued mediators
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矩阵值中介变量的贝叶斯联合中介分析模型
- 研究问题
- 如何将保留矩阵结构的高维剂量信息作为中介，分解放疗处方剂量对治疗中断的影响，并定位哪些矩阵指标参与中介路径？
- 方法与思路
- 作者用改造的概率多线性主成分分析提取矩阵潜在特征，建立贝叶斯联合中介模型，以 Gibbs 抽样估计并用 Varimax 旋转识别指标。
- 材料陈述的贡献
- 摘要称模拟中联合模型估计因果分解效应比所比较的两步方法更有效率，并能以矩阵形式呈现中介效应，应用于肛管癌患者。
- 阅读关注点
- 应核对因果分解的识别假设、潜在特征与旋转后的解释，以及联合抽样收敛、模拟效率评价和应用中中介指标的不确定性。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未说明因果识别条件、先验选择或效率提升的具体幅度。
原文摘要
Unscheduled treatment interruptions may lead to reduced quality of care in radiation therapy (RT). Identifying the RT prescription dose effects on the outcome of treatment interruptions, mediated through doses distributed into different organs-at-risk (OARs), can inform future treatment planning. The radiation exposure to OARs can be summarized by a matrix of dose-volume histograms (DVH) for each patient. Although various methods for high-dimensional mediation analysis have been proposed recently, few studies investigated how matrix-valued data can be treated as mediators. In this paper, we propose a novel Bayesian joint mediation model for high-dimensional matrix-valued mediators. In this joint model, latent features are extracted from the matrix-valued data through an adaptation of probabilistic multilinear principal components analysis (MPCA), retaining the inherent matrix structure. We derive and implement a Gibbs sampling algorithm to jointly estimate all model parameters, and introduce a Varimax rotation method to identify active indicators of mediation among the matrix-valued data. Our simulation study finds that the proposed joint model has higher efficiency in estimating causal decomposition effects compared to an alternative two-step method, and demonstrates that the mediation effects can be identified and visualized in the matrix form. We apply the method to study the effect of prescription dose on treatment interruptions in anal canal cancer patients.