A Gaussian copula joint model for longitudinal and time-to-event data with random effects
针对纵向数据和事件时间数据联合建模中条件独立性假设难以验证的问题，提出一种含随机效应的高斯连接函数联合模型，该模型在关联参数为零时退化为传统条件独立模型，并通过模拟和实际数据验证了其性能。
Longitudinal and survival sub-models are two building blocks for joint modelling of longitudinal and time-to-event data. Extensive research indicates separate analysis of these two processes could result in biased outputs due to their associations. Conditional independence between measurements of biomarkers and event time process given latent classes or random effects is a conventional approach for characterising the association between the two sub-models while taking the heterogeneity among the population into account. However, this assumption is tricky to validate because of the unobservable latent variables. Thus a Gaussian copula joint model with random effects is proposed to accommodate the scenarios where the conditional independence assumption is questionable. The conventional joint model assuming conditional independence is a special case of the proposed model when the association parameters in the Gaussian copula shrink to zero. Simulation studies and real data application are carried out to evaluate the performance of the proposed model with different correlation structures. In addition, personalised dynamic predictions of survival probabilities are obtained based on the proposed model and comparisons are made to the predictions obtained under the conventional joint model.