This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 390
Type: Topic Contributed
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #307682
Title: Bayesian Median Regression for Censored Survival Data via Transform-Both-Sides Model
Author(s): Debajyoti Sinha*+ and Jian-Chang Lin and Stuart R. Lipsitz
Companies: Florida State University and Florida State University and Brigham and Women's Hospital
Address: Department of Statistics, Florida State University, Tallahassee, FL, 3064330, USA
Keywords: Transform-both-sides model ; Dirichlet process ; survival analysis

For survival data, median regression offers an useful alternative to the methods based on popular proportional hazards and accelerated failure models. Existing frquentist median regression methods for survival data often require restrictive assumptions and hard to implement complex algorithms involving nonlinear and discontinuous equations. We propose new parametric and semiparametric Bayes methods for estimating the parameters of median regression using a model with same optimal and unknown transformation to both log-survival and median. Via theoretical and simulation studies, we show better performance and properties of our estimators compared to existing estimators in various situations including where our modeling assumption is not correct. We illustrate the implementation of our estimation estimation methods and model diagnostics via reanalysis of a small-cell lung cancer study.

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