使用R包INLAjoint对多元纵向与生存结局进行联合建模
Joint Modeling of Multivariate Longitudinal and Survival Outcomes with the R package INLAjoint
AI总结：
本文介绍R包INLAjoint，它基于集成嵌套拉普拉斯近似实现贝叶斯推断，以模块化方式构建纵向与生存结局的联合模型，用于纠正单独建模中的偏倚，并通过示例展示其在生物医学复杂数据中的应用价值。
摘要：
本文介绍了R包INLAjoint，该包被设计为一个工具箱，用于拟合多种同时处理纵向结局和生存结局的回归模型。INLAjoint依赖于集成嵌套拉普拉斯近似（integrated nested Laplace approximations）方法的计算效率，该方法作为贝叶斯推断中马尔可夫链蒙特卡洛（Markov chain Monte Carlo）的一种高效替代方案，能够在参数估计和不确定性量化中同时保证速度与准确性。该包通过将各个回归模型视为构建模块来促进复杂联合模型的构建，这些模块可以被组装起来以解决特定的研究问题。联合模型在生物医学研究中具有重要价值，因为在这些研究中，纵向标志物与删失生存时间的收集十分常见。近年来，联合模型在文献中引起了广泛关注，并展示了其纠正单独建模方法中存在的偏倚的能力，例如由生存事件引起的信息性删失，或由于人群异质性导致的混淆偏倚。我们对嵌入在INLAjoint中的联合建模框架进行了全面概述，并提供了说明性示例。通过这些示例，我们展示了INLAjoint在处理生物医学研究中遇到的复杂数据场景方面的实际效用。
英文摘要：
This paper introduces the R package INLAjoint, designed as a toolbox for fitting a diverse range of regression models addressing both longitudinal and survival outcomes. INLAjoint relies on the computational efficiency of the integrated nested Laplace approximations methodology, an efficient alternative to Markov chain Monte Carlo for Bayesian inference, ensuring both speed and accuracy in parameter estimation and uncertainty quantification. The package facilitates the construction of complex joint models by treating individual regression models as building blocks, which can be assembled to address specific research questions. Joint models are relevant in biomedical studies where the collection of longitudinal markers alongside censored survival times is common. They have gained significant interest in recent literature, demonstrating the ability to rectify biases present in separate modeling approaches such as informative censoring by a survival event or confusion bias due to population heterogeneity. We provide a comprehensive overview of the joint modeling framework embedded in INLAjoint with illustrative examples. Through these examples, we demonstrate the practical utility of INLAjoint in handling complex data scenarios encountered in biomedical research.
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