Samiran SinhaThursday, November 3, 2016 - 2:45pm
Statistics Department Colloquium
Location: LeConte College, Room 210
In this talk we consider fitting an AFT model to right censored data when a predictor variable is subject to measurement errors. It is well know that without measurement errors, estimation of the model parameters in the AFT model is a challenging task due to the presence of censoring, especially when no specific assumption is made regarding the distribution of the logarithm of the time-to-event. The model complexity increases when a predictor is measured with error. We propose a nonparametric Bayesian method for analyzing such data. The novel component of our approach is to model 1) the distribution of the time-to-event, 2) the distribution of the unobserved true predictor, and 3) the distribution of the measurement errors all nonparametrically using mixtures of the Dirichlet process priors. Along with the parameter estimation we also prescribe how to estimate survival probabilities of the time-to-event. We illustrate the usefulness of the proposed method through limited simulation studies. Finally, we will discuss an application of the proposed method by analyzing a data set from an AIDS clinical trial study.
Errors-in-covariates in the Accelerated Failure Time Model--Samiran Sinha