JSM 2005 Online Program

Abstract #304691

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	447
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor:	General Methodology
Abstract - #304691
Title:	Analysis of Interval-censored Survival Data in the Presence of Informative Examination Times Using an EM Algorithm
Author(s):	Anjela Tzontcheva*+ and James Stafford
Companies:	University of Toronto and University of Toronto
Address:	20 Vanauley Street, Toronto, ON, M5T 2H4, Canada
Keywords:	EM algorithm ; interval-censored data ; informative examination times
Abstract:	Interval-censored survival data in the presence of informative examination times arise in the case of the human immunodeficiency virus (HIV). In HIV patients, the precise seroconversion time is only known to lie within a time interval, and in certain subpopulations, visits may be more frequent due to a greater (unobservable) risk. Therefore, heterogeneity in HIV incidence will likely be reflected in test visits frequency. If ignored, such heterogeneity may lead to biased incidence estimates and invalid inference results. Farrington and Gay (1999) analyzed interval-censored data with informative examination times using individual-specific frailties to capture the correlation between an individual's risk and visit rate. However, their proposed methodology is based on a Laplace approximation of the marginal log-likelihood. Here, we present an estimation algorithm based on imputation of survival times that avoids the embedded Laplace approximation. We employ EM algorithm to obtain parameter estimates and evaluate the bias and efficiency of our method via simulations. Under modeling scenarios, our approach results in reduced bias compared to Farrington and Gay's method.

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