Nonparametric inference for reversed mean models with panel count data.

ABSTRACT: Panel count data typically refer to data arising from studies with recurrent events, in which subjects are observed only at discrete time points rather than under continuous observations. We investigate a general situation where a recurrent event process is eventually truncated by an informative terminal event and we are particularly interested in behaviors of the recurrent event process near the terminal event. We propose a reversed mean model for estimating the mean function of the recurrent event process. We develop a two-stage sieve likelihood-based method to estimate the mean function, which overcomes the computational difficulties arising from a nuisance functional parameter involved in the likelihood. The consistency and the convergence rate of the two-stage estimator are establishe

SUBMITTER: Liu LI 

PROVIDER: S-EPMC12030208 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature