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Clustered multistate process data are commonly encountered in multicenterobservational studies and clinical trials. A clinically important estimand withsuch data is the marginal probability of being in a particular transient stateas a function of time. However, there is currently no method for nonparametricmarginal regression analysis of these probabilities with clustered multistateprocess data. To address this problem, we propose a weighted functionalgeneralized estimating equations approach which does not impose Markovassumptions or assumptions regarding the structure of the within-clusterdependence, and allows for informative cluster size (ICS). The asymptoticproperties of the proposed estimators for the functional regressioncoefficients are rigorously established and a nonparametric hypothesis testingprocedure for covariate effects is proposed. Simulation studies show that theproposed method performs well even with a small number of clusters, and thatignoring the within-cluster dependence and the ICS leads to invalid inferences.The proposed method is used to analyze data from a multicenter clinical trialon recurrent or metastatic squamous-cell carcinoma of the head and neck with astratified randomization design.

Marginal Regression on Transient State Occupation Probabilities with Clustered Multistate Process Data