FastJM：用于高效实现纵向与生存数据半参数联合模型的R包
FastJM: An R Package for Efficient Implementation of Semiparametric Joint Models for Longitudinal and Survival Data
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中文总结 AI 辅助
FastJM是用于高效实现三类半参数联合模型的R包，采用定制线性扫描算法解决计算瓶颈，提供统一接口支持模型相关操作及动态预测等功能。
中文摘要 AI 辅助
联合模型提供了刻画纵向过程与时间-事件过程之间关联的灵活框架，已被广泛应用于生物医学研究。然而，针对大规模复杂生物医学数据拟合联合模型可能面临计算挑战。本文介绍R包FastJM，它为三类半参数联合模型提供计算高效的频率学派估计：含单个纵向生物标志物的联合模型、含多个纵向生物标志物的联合模型，以及含具有异质组内（WS）变异性的单个纵向生物标志物的联合模型。在期望-最大化框架内，FastJM采用定制的线性扫描算法高效更新非参数基准风险，从而解决了半参数联合建模中的一个主要计算瓶颈。该包还通过将这些算法与地标多变量联合建模框架集成，支持常用的时间依赖潜在关联结构。FastJM为模型设定、估计、推断、可视化、动态预测及预测性能评估提供统一接口，包括交叉验证的时间依赖准确性度量和时间独立一致性统计量。我们阐述了其底层方法与软件实现，并通过可复现示例展示FastJM的主要功能。
英文摘要
Joint models provide a flexible framework for characterizing the association between longitudinal and time-to-event processes and have been widely applied in biomedical research. However, fitting joint models can be computationally challenging for large-scale and complex biomedical data. This paper introduces the \proglang{R} package \pkg{FastJM}, which provides computationally efficient frequentist estimation for three classes of semiparametric joint models: joint models with a single longitudinal biomarker, joint models with multiple longitudinal biomarkers, and joint models with a single longitudinal biomarker with heterogeneous within-subject (WS) variability. Within an expectation--maximization framework, \pkg{FastJM} employs customized linear-scan algorithms to efficiently update the nonparametric baseline hazards, thereby addressing a major computational bottleneck in semiparametric joint modeling. The package also supports commonly used time-dependent latent association structures by integrating these algorithms with a landmark multivariate joint modeling framework. \pkg{FastJM} provides a unified interface for model specification, estimation, inference, visualization, dynamic prediction, and prediction performance assessment, including cross-validated time-dependent accuracy measures and time-independent concordance statistics. We describe the underlying methodology and software implementation and demonstrate the main functionality of \pkg{FastJM} through reproducible examples.
