Informative cluster size (ICS) is a phenomenon where cluster size is related to the outcome. While multistate models can be applied to characterize the unit‐level transition process for clustered interval‐censored data, there is a research gap addressing ICS within this framework. We propose two extensions of multistate model that account for ICS to make marginal inference: one by incorporating within‐cluster resampling and another by constructing cluster‐weighted score functions. We evaluate the performances of the proposed methods through simulation studies and apply them to the Veterans Affairs Dental Longitudinal Study (VADLS) to understand the effect of risk factors on periodontal disease progression. ICS occurs frequently in dental data, particularly in the study of periodontal disease, as people with fewer teeth due to the disease are more susceptible to disease progression. According to the simulation results, the mean estimates of the parameters obtained from the proposed methods are close to the true values, but methods that ignore ICS can lead to substantial bias. Our proposed methods for clustered multistate model are able to appropriately take ICS into account when making marginal inference of a typical unit from a randomly sampled cluster.

中文翻译：

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具有信息簇大小的纵向渐进过程的边缘簇多状态模型

信息簇大小（ICS）是一种簇大小与结果相关的现象。虽然多状态模型可用于表征集群区间删失数据的单元级转换过程，但在该框架内解决 ICS 方面存在研究空白。我们提出了多状态模型的两种扩展，用于解释 ICS 进行边际推理：一种是通过合并簇内重采样，另一种是通过构建簇加权评分函数。我们通过模拟研究评估所提出方法的性能，并将其应用于退伍军人事务部牙科纵向研究（VADLS），以了解危险因素对牙周病进展的影响。ICS 经常出现在牙科数据中，特别是在牙周病的研究中，因为因疾病而牙齿较少的人更容易受到疾病进展的影响。根据仿真结果，所提出的方法获得的参数的平均估计值接近真实值，但是忽略ICS的方法可能会导致很大的偏差。我们提出的聚类多状态模型方法能够在从随机采样的聚类中对典型单元进行边际推断时适当地考虑 ICS。