发表机构
Univ. Bordeaux; INSERM U1219; Bordeaux Population Health（波尔多大学; INSERM U1219; 波尔多人群健康）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
LSJM R包基于位置-尺度混合模型和联合模型理论，拟合纵向标记与复杂生存数据（含竞争风险、半竞争风险及区间删失）的联合模型，提供灵活相关结构、严格收敛估计及拟合后分析功能。
AI 中文摘要
LSJM R包提供了一系列基于位置-尺度线性混合模型和位置-尺度联合模型理论的模型估计函数。对于纵向数据，它包含了具有受试者特异性残差方差的位置-尺度混合模型的估计，该残差方差可以是时间依赖的，或区分访视内和访视间的变异性。当考虑生存数据时，所实现的联合模型处理竞争风险以及半竞争风险，可能通过使用疾病-死亡子模型处理区间删失数据。它们通过当前值、斜率和/或标记的残差变异性，在纵向标记和事件时间之间纳入灵活的相关结构。使用Marquardt-Levenberg算法并采用严格收敛准则获得最大似然估计。该包还提供了多种拟合后函数，包括拟合优度分析和个体动态预测。通过真实数据的一些例子说明了该包的使用。
英文摘要
The LSJM R package provides a series of functions to estimate models based on location-scale linear mixed model and location-scale joint model theory. For longitudinal data, it includes the estimation of location-scale mixed models with subject-specific residual variance that can be time-dependent or distinguish within-visit and between-visits variability. When survival data is considered, implemented joint models handle competing risks as well as semi-competing risks possibly with interval-censored data through the use of a illness-death sub-model. They incorporate flexible dependence structure between the longitudinal marker and the times-to-events through the current value, the slope and/or the residual variability of the marker. Maximum likelihood estimators are obtained using the Marquardt-Levenberg algorithm with stringent convergence criteria. The package also provides several post-fit functions including goodness-of-fit analyses and individual dynamic predictions. Some examples on real data allows to illustrates the use of this package.
