We propose a multistate joint model to analyze interval‐censored event‐history data subject to within‐unit clustering and nonignorable missing data. The model is motivated by a study of the neurocysticercosis (NC) cyst evolution at the cyst‐level, taking into account the multiple cysts phases with intermittent missing data and loss to follow‐up, as well as the intra‐brain clustering of observations made on a predefined data collection schedule. Of particular interest in this study is the description of the process leading to cyst resolution, and whether this process varies by antiparasitic treatment. The model uses shared random effects to account for within‐brain correlation and to explain the hidden heterogeneity governing the missing data mechanism. We developed a likelihood‐based method using a Monte Carlo EM algorithm for the inference. The practical utility of the methods is illustrated using data from a randomized controlled trial on the effect of antiparasitic treatment with albendazole on NC cysts among patients from six hospitals in Ecuador. Simulation results demonstrate that the proposed methods perform well in the finite sample and misspecified models that ignore the data complexities could lead to substantial biases.

中文翻译：

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用于受单元内聚类和信息缺失影响的区间审查事件历史数据的多状态联合模型，适用于神经囊尾蚴病研究。

我们提出了一个多状态联合模型来分析区间删失的事件历史数据，这些数据受到单元内聚类和不可忽略的缺失数据的影响。该模型的动机是对囊肿水平的神经囊尾蚴病 (NC) 囊肿演变的研究，考虑到具有间歇性缺失数据和失访的多个囊肿阶段，以及观察到的脑内聚类根据预定义的数据收集计划。本研究特别感兴趣的是描述导致囊肿消退的过程，以及该过程是否因抗寄生虫治疗而异。该模型使用共享随机效应来解释脑内相关性并解释控制缺失数据机制的隐藏异质性。我们开发了一种基于似然的方法，使用 Monte Carlo EM 算法进行推理。使用来自厄瓜多尔六家医院患者的阿苯达唑抗寄生虫治疗对 NC 囊肿效果的随机对照试验数据说明了这些方法的实际用途。仿真结果表明，所提出的方法在有限样本中表现良好，而忽略数据复杂性的错误指定模型可能会导致大量偏差。