Title: A general class of additive semiparametric models for recurrent event data
Authors: Russell Stocker - Indiana University of Pennsylvania (United States)
Akim Adekpedjou - Missouri University of Science and Technology (United States) [presenting]
Abstract: Recurrent event data is a special case of multivariate lifetime data that is present in a large assortment of studies. Due to its pervasiveness, it is essential that appropriate models and inference procedures exist for its analysis. We propose a general class of additive semiparametric models for examining recurrent event data that uses an effective age process to take into account the impact of interventions applied to units after an event occurrence. The effect of covariates is additive instead of the common multiplicative assumption.We derive estimators of the regression parameter, baseline hazard function, and baseline survivor function. We also establish the asymptotic properties of the estimators using tools from empirical process theory. Simulation studies indicate that the asymptotic properties of the regression parameter closely approximate its finite sample properties. The analysis of a real data set consisting of lymphoma recurrence times provides a practical illustration of the class of models.