A0426
Title: Nonparametric inference for reversed mean models with panel count data
Authors: Li Liu - Wuhan University (China) [presenting]
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. A general situation where a recurrent event process is eventually truncated by an informative terminal event is investigated, and the interest is in behaviors of the recurrent event process near the terminal event. A reversed mean model is proposed for estimating the mean function of the recurrent event process. A two-stage sieve likelihood-based method is developed 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 established. Allowing for the convergence rate to be slower than the standard rate, the general weak convergence theory of M-estimators is developed with a nuisance functional parameter and then applied to the proposed estimator for deriving the asymptotic normality. Furthermore, a class of two-sample tests is developed. The proposed methods are evaluated with extensive simulation studies and illustrated with panel count data from the Chinese Longitudinal Healthy Longevity Study.
