论文标题
纵向和生存数据的联合潜在类模型，其成员概率与时变概率
A joint latent class model of longitudinal and survival data with a time-varying membership probability
论文作者
论文摘要
在过去的二十年中，共同开发了潜在的阶级建模。在某些情况下，模型由潜在类K（即子组的数量）链接，在其他情况下，它们通过共享随机效应或异质随机协方差矩阵连接。我们提出了一个联合潜在类模型（JLCM）的扩展名，在该模型中，可以将受试者的概率设置为随时间变化。这可能是一种分析对患者治疗的影响的更灵活的方法。例如，患者可能是在第一次访问时在I期间，并且可能在第二次访问时移至第二阶段，这意味着患者以前的治疗可能在以下访问时没有表现。对于具有这些特定功能的数据集，与基本JLCM相比，允许不同子组之间跳跃的联合潜在类模型可能会提供更多信息，以及更准确的估计和预测结果。贝叶斯方法用于进行估计，并使用DIC标准来确定最佳类数。仿真结果表明，所提出的模型产生准确的结果，而随时间变化的JLCM优于基本JLCM。我们还说明了我们提出的JLCM在AIDS数据上的性能（Goldman等，1996）。
Joint latent class modelling has been developed considerably in the past two decades. In some instances, the models are linked by the latent class k (i.e. the number of subgroups), in others they are joined by shared random effects or a heterogeneous random covariance matrix. We propose an extension to the joint latent class model (JLCM) in which probabilities of subjects being in latent class k can be set to vary with time. This can be a more flexible way to analyse the effect of treatments to patients. For example, a patient may be in period I at the first visit time and may move to period II at the second visit time, implying the treatment the patient had before might be noneffective at the following visit time. For a dataset with these particular features, the joint latent class model which allows jumps among different subgroups can potentially provide more information as well as more accurate estimation and prediction results compared to the basic JLCM. A Bayesian approach is used to do the estimation and a DIC criterion is used to decide the optimal number of classes. Simulation results indicate that the proposed model produces accurate results and the time-varying JLCM outperforms the basic JLCM. We also illustrate the performance of our proposed JLCM on the aids data (Goldman et al., 1996).
