STATISTICS COLLOQUIUM
Jun Yan, Professor
Department of Statistics
University of Connecticut
Generalized scale-change models for recurrent event processes under informative censoring
Abstract
Two major challenges arise in regression analyses of recurrent event data: first, popular existing models, such as the Cox-type models, may not fully capture the covariate effects on the underlying recurrent event process; second, the censoring time remains informative about the risk of experiencing recurrent events after accounting for covariates. We tackle both challenges by a general class of semiparametric scale-change models that allow a scale-change covariate effect as well as a multiplicative covariate effect. The proposed model is flexible and nests several existing models, including the popular proportional rates model, the accelerated mean model, and the accelerated rate model. Moreover, it accommodates informative censoring through subject-level latent frailty whose distribution is left unspecified. A robust approach is proposed to estimate the model parameters, which does not need a parametric assumption on the distribution of the frailty and the recurrent event process. The asymptotic properties of the resulting estimator are established, with the asymptotic variance estimated from a novel resampling approach. As a byproduct, the structure of the model provides a model selection approach among the submodels via hypothesis testing of model parameters. Numerical studies show that the proposed estimator and the model selection procedure perform well under both noninformative and informative censoring scenarios. The methods are applied to data from two transplant cohorts to study the risk of infections after transplantation.
DATE: Wednesday, February 13, 2019
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 108
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu