Report title: Regression analysis of dependent current status data with the accelerate failure time model

Reporter: Xu Da, PhD, Northeast Normal University

Reporting time: December 24, 2020, 14:00-15:00 pm

Report location: First Lecture Hall, Mathematics Building

Report summary:

In this article, we discuss the regression analysis of dependent current status data under the accelerated failure time model. There exist many literatures discussing the regression analysis of current status data under different models, but few literature discussing the regression problem of dependent current status data under the AFT model. Corresponding to this, we propose a sieve maximum likelihood approach for estimation of covariate effects. In the approach, we model the correlation between the interested survival time and the observation time by the copula function. Simulation study is conducted in order to assess the finite sample behavior of the method. A real data example is provided to illustrate the application of the proposed method.

Brief introduction of the speaker:

Xu Da is a lecturer in the School of Mathematics and Statistics, Northeast Normal University. He received a PhD in Probability Theory and Mathematical Statistics from the School of Mathematics, Jilin University. The main research direction is survival analysis, length deviation data, semi-parametric non-parametric modeling inference of complex data, etc. He has published many papers in well-known journals at home and abroad.