JSM 2005 Online Program

Abstract #303359

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:	200
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Biometrics Section
Abstract - #303359
Title:	Group Testing Model Estimation and Inference
Author(s):	Christopher R. Bilder*+
Companies:	University of Nebraska
Address:	Department of Statistics, Lincoln, NE, 68583, United States
Keywords:	binary ; prevalence ; diagnostic test ; pooling ; generalized linear model ; unobservable
Abstract:	Group testing has long been used to estimate a trait prevalence, p, in situations where the prevalence is small in order to reduce time and cost or to make infeasible individual experiments feasible by grouping. Most of the statistical research in group testing has focused on estimating a single prevalence p for a homogenous population. Recently, Vansteelandt et al. (2000) and Xie (2001) proposed models to incorporate covariates to estimate p for a heterogeneous population. The purpose here is to further examine these modeling methods through a set of comparisons between individual and group testing models. First, the relative efficiency of model parameter estimates is investigated under a number of grouping strategies. Second, agreement between model parameter estimates is examined to determine how well estimates coincide. Third, the effect of group size on model estimation is examined. Overall recommendations are given in order to show the benefits and sacrifices of using group testing models.

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