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Title: Semiparametric estimation of the accelerated mean model with panel count data under informative examination times Authors:  Sy Han Chiou - University of Texas at Dallas (United States) [presenting]
Gongjun Xu - University of Michigan (United States)
Chiung-Yu Huang - University of California, San Francisco (United States)
Jun Yan - University of Connecticut (United States)
Abstract: Panel count data arise when the number of recurrent events experienced by each study subject is observed intermittently at discrete examination times. The validity of existing methods usually requires the examination time process being independent of the underlying recurrent event process; however, this independence assumption fails to hold in many applications. We consider a semiparametric accelerated mean model for the underlying recurrent event process and allow the two processes to be correlated through shared frailty. The model allows the regression parameters to have a simple marginal interpretation of modifying the time scale of the cumulative mean function of the event process. A novel estimation procedure for the regression parameters and the baseline rate function is proposed. In contrast to existing methods, the proposed method is robust in the sense that it requires neither the strong Poisson-type assumption for the underlying recurrent event process nor a parametric assumption on the distribution of the unobserved frailty. The asymptotic consistency of the estimator is established, and the variance of the estimator is estimated by a model-based smoothed bootstrap procedure. Numerical studies demonstrated that the proposed point estimator and variance estimator performs well with practical sample sizes. The methods are applied to data from a skin cancer chemoprevention trial.