Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA.
Biometrics. 2022 Sep;78(3):1233-1243. doi: 10.1111/biom.13475. Epub 2021 May 4.
Longitudinal biomarkers are widely used in biomedical and translational researches to monitor the progressions of diseases. Methods have been proposed to jointly model longitudinal data and survival data, but its causal mechanism is yet to be investigated rigorously. Understanding how much of the total treatment effect is through the biomarker is important in understanding the treatment mechanism and evaluating the biomarker. In this work, we propose a causal mediation analysis method to compute the direct and indirect effects, when a joint modeling approach is used to take the longitudinal biomarker as the mediator and the survival endpoint as the outcome. Such a joint modeling approach allows us to relax the commonly used "sequential ignorability" assumption. We demonstrate how to evaluate longitudinally measured biomarkers using our method with two case studies, an AIDS study and a liver cirrhosis study.
纵向生物标志物广泛应用于生物医学和转化研究中，以监测疾病的进展。已经提出了一些方法来联合建模纵向数据和生存数据，但它的因果机制仍需要严格地研究。了解治疗效果中有多少是通过生物标志物实现的，对于理解治疗机制和评估生物标志物非常重要。在这项工作中，我们提出了一种因果中介分析方法，以计算当联合建模方法将纵向生物标志物作为中介，生存终点作为结果时的直接和间接效应。这种联合建模方法允许我们放宽常用的“顺序可忽略性”假设。我们通过两个案例研究，即艾滋病研究和肝硬化研究，展示了如何使用我们的方法来评估纵向测量的生物标志物。
