JSM 2005 Online Program

Abstract #304389

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.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter

Activity Number:	455
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Statistics in Epidemiology
Abstract - #304389
Title:	Hierarchical Latent Class Model for Evaluating Diagnostic Tests
Author(s):	Alula Hadgu*+ and Nandini Dendukuri
Companies:	Centers for Disease Control and Prevention and McGill University
Address:	1600 Clifton Road, Atlanta, GA, 30333,
Keywords:	Diagnostic tests ; Bayesian inference ; Latent class model ; Conditional dependence ; Sexually transmitted disease
Abstract:	Studies of the validity of a new diagnostic test typically involve comparison of the new tests' results with an imperfect gold-standard test. Latent class models have been used widely for the analysis of such data, their advantage being that they do not assume 100% sensitivity and specificity of the imperfect test. While the traditional latent class model assumes different tests are conditionally independent given the latent true disease status, various extensions have been proposed to relax this unrealistic assumption. In this presentation, we describe another method for modeling conditional dependence between tests that separates groups of tests based on different biological phenomena. For the parameter estimation, as well as model checking and comparison, we use a Bayesian approach. The methods are illustrated using results from a study of diagnostic tests for a sexually transmitted disease.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program