Multistate current status (CS) data presents a more severe form of censoring due to the single observation of study participants transitioning through a sequence of well-defined disease states at random inspection times. Moreover, these data may be clustered within specified groups, and informativeness of the cluster sizes may arise due to the existing latent relationship between the transition outcomes and the cluster sizes. Failure to adjust for this informativeness may lead to a biased inference. Motivated by a clinical study of periodontal disease (PD), we propose an extension of the pseudo-value approach to estimate covariate effects on the state occupation probabilities (SOP) for these clustered multistate CS data with informative cluster or intra-cluster group sizes. In our approach, the proposed pseudo-value technique initially computes marginal estimators of the SOP utilizing nonparametric regression. Next, the estimating equations based on the corresponding pseudo-values are reweighted by functions of the cluster sizes to adjust for informativeness. We perform a variety of simulation studies to study the properties of our pseudo-value regression based on the nonparametric marginal estimators under different scenarios of informativeness. For illustration, the method is applied to the motivating PD dataset, which encapsulates the complex data-generation mechanism.

翻译：多状态当前状态数据由于在随机检查时间仅观察研究对象经历一系列明确定义疾病状态的单一观测，呈现出一种更为严重的删失形式。此外，这些数据可能在特定组内聚类，且由于转换结果与簇规模之间存在的潜在隐关系，可能导致簇规模的信息性。未能调整这种信息性可能导致有偏推断。受牙周病临床研究的启发，我们提出了一种伪值方法的扩展，以估计协变量对具有信息性簇或簇内组规模的成组多状态当前状态数据的状态占用概率的影响。在我们的方法中，所提出的伪值技术首先利用非参数回归计算状态占用概率的边际估计量。接着，基于相应伪值的估计方程通过簇规模的函数重新加权，以调整信息性。我们进行了多种模拟研究，以研究在不同信息性场景下基于非参数边际估计量的伪值回归的性质。为进行说明，该方法应用于激励性牙周病数据集，该数据集封装了复杂的数据生成机制。