Clustered multistate process data are commonly encountered in multicenter observational studies and clinical trials. A clinically important estimand with such data is the marginal probability of being in a particular transient state as a function of time. However, there is currently no method for nonparametric marginal regression analysis of these probabilities with clustered multistate process data. To address this problem, we propose a weighted functional generalized estimating equations approach which does not impose Markov assumptions or assumptions regarding the structure of the within-cluster dependence, and allows for informative cluster size (ICS). The asymptotic properties of the proposed estimators for the functional regression coefficients are rigorously established and a nonparametric hypothesis testing procedure for covariate effects is proposed. Simulation studies show that the proposed method performs well even with a small number of clusters, and that ignoring the within-cluster dependence and the ICS leads to invalid inferences. The proposed method is used to analyze data from a multicenter clinical trial on recurrent or metastatic squamous-cell carcinoma of the head and neck with a stratified randomization design.

翻译：多中心观测研究和临床试验中通常会遇到多层集束化的多状态过程数据。临床上非常重要的数据估计,其边缘概率是作为时间函数处于特定瞬态的边缘概率。然而,目前没有方法用多层集束化的多状态过程数据对这些概率进行非对数边回归分析。为解决这一问题,我们建议采用加权功能通用估计方程方法,该方法不强制设定关于集群内依赖结构的假设或假设,也不容许提供信息的集群大小(ICS)。拟议功能回归系数估计值的无症状特性得到了严格确立,并提出了共变效应的非参数假设测试程序。模拟研究表明,拟议方法即使与少量的集群相配合,也运行良好,忽视集群内依赖性和ICS导致无效推断。拟议方法用于分析关于头颈部和颈部经常或向导式对立细胞癌的多点临床试验数据,并进行分层随机设计。