University of Missouri at Columbia
Thursday, February 21, 2013
Richardson Hall 224
"Regression Analysis of Panel Count Data with Dependent Observation Process and Terminal Event"
Abstract: Panel count data occur in many fields and a number of approaches have been developed. However, most of these approaches are for the situations where there is no terminal event and the observation process is independent of the underlying recurrent event process unconditionally or conditional on the covariates. In this talk, we discuss a more general situation where the observation process is informative and there exists a terminal event that precludes further occurrence of the recurrent events of interest. For the analysis, a semiparametric transformation model is presented for the mean function of the underlying recurrent event process among survivors. To estimate regression parameters, an estimating equation approach is proposed in which an inverse survival probability weighting technique is used. The methodology is assessed by simulation studies and an application, which motivated this study, is discussed.