Joshua M. Tebbs, 
Department of Statistics, 
University of South Carolina


Group testing regression models with fixed and random effects

Abstract

Group testing, where subjects are tested in pools rather than individually, has a 
long history of successful application in infectious disease screening. In this paper, 
we develop group testing regression models to include covariate effects which are best 
regarded as random. We present approaches to fit mixed effects models using maximum 
likelihood, investigate likelihood ratio and score tests for variance components, and 
evaluate small sample performance using simulation. We illustrate our methods using 
chlamydia and gonorrhea data collected by the state of Nebraska as part of the 
Infertility Prevention Project.