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).
翻译:在过去二十年中,联合潜伏类建模有了相当大的发展,在某些情况下,模型与潜伏类k(即分组数目)相联系,在另一些情况下,模型与隐伏类K(即分组数目)相联,同时有共同随机效应或杂异随机共变矩阵。我们建议扩展联合潜伏类模型(JLCM),在这种模型中,潜伏类K的主体概率可以定得随着时间的变化而变化。这可以成为分析治疗对病人影响的一种更灵活的方法。例如,病人在第一次访问时可能处于第一阶段,在第二次访问时可能进入第二阶段,这意味着患者以前曾接受的治疗在下次访问时可能无效。对于具有这些特点的数据集,允许不同分组跳跃的联合潜伏类模型可以提供更多信息,以及更准确的估算和预测结果,采用巴伊西亚方法进行估计,并使用DIC标准决定最佳的班数。模拟结果显示,拟议的模型产生准确的结果,而JLCM和JRC模拟了我们1996年拟议的基本性能(JLCM)。</s>