Xianoyan (Iris) Lin
Department of Statistics
University of South Carolina
A Semiparametric Probit Model for Interval-Censored Failure Time Data
The analysis of time-to-event data, generally called survival analysis,
arises in many fields of study, including medicine, biology, engineering,
public health, epidemiology, and economics. One important feature of
survival data is that the failure time of interest is incomplete due to some
censoring mechanism. Interval-censored data is a special type of survival
data containing left, interval, and right-censored observations. Due to its
complex data structure and censoring mechanism, many conventional methods
including counting process and martingale techniques, which are successfully
used for right-censored data, are not applicable. In this talk, I will
present a semi-parametric probit model for analyzing general
interval-censored data. We propose to model the unknown non-decreasing
function with monotone splines, and develop both Bayesian and likelihood
methods under the model. The proposed Bayesian method relies on a novel data
augmentation based on the interval-censored data structure and is very easy
to implement. Both Bayesian and likelihood methods work very well in the
simulation studies and data applications.
The ideas used in the proposed approaches can be generalized to other
semi-parametric models for analyzing interval-censored data. I will briefly
present some ongoing projects on such extensions.
Back to Colloquium Series