Group testing regression models with fixed and random effects.
Group testing, where subjects are tested in pools rather than individually, has a long history of successful application in infectious disease screening. In this article, we develop group testing regression models to include covariate effects that 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.
Chen
Peng
P
Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, USA.
Tebbs
Joshua M
JM
Bilder
Christopher R
CR
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D002690:Chlamydia Infections / Q000453:epidemiology; D016208:Databases, Factual; D005260:Female; D006069:Gonorrhea / Q000453:epidemiology; D006801:Humans; D007246:Infertility / Q000517:prevention & control; D016013:Likelihood Functions; D016014:Linear Models; D008297:Male; D009010:Monte Carlo Method; D009329:Nebraska / Q000453:epidemiology; D012044:Regression Analysis