Georgia Health Sciences University
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We propose nonparametric estimators for the state occupation probabilities in a given state adjusting for the informative cluster size and one covariate at a time in a multistate model. This is a non-trivial problem since the state occupied is determined at a single inspection time for each subject and a group of subjects belongs to a cluster where cluster size is informative to their state status. Nonparametric weighted monotonic regression and smoothing are used to uniquely define the state occupation probability weighted by the inverse of cluster size at a pre-specified value of the covariate. A detailed simulation study evaluated the global performance of the proposed nonparametric estimator. An illustrative application to a dental disease data is also presented.