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

Abstract Details

Activity Number: 624
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #308555
Title: Semiparametric Linear Transformation Models for Interval-Censored Failure Time Data
Author(s): Bin Zhang*+ and Lianming Wang and Zhigang Zhang
Companies: The University of Alabama at Birmingham and University of South Carolina and Memorial Sloan-Kettering Cancer Center
Address: 1029 Aspen Run, Birmingham, AL, 35209, U.S.
Keywords: case 2 interval-censored data ; linear transformation model ; maximum likelihood method ; monotone spline ; semiparametric models

Interval-censored data occur when the failure time is often not observed directly, but only known to lie within an interval. In this paper, we propose a novel semiparametric linear transformation model for analyzing case 2 interval-censored data. Linear transformation models provide a class of flexible models for regression analysis of failure time data. Here the unknown nonparametric cumulative hazard function in the linear transformation models is approximated with a linear combination of monotone splines. Different penalized estimating functions are also considered for spline selection. Simulation studies show that the approach is appropriate for practical use and several applications are given to illustrate the approach.

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