Maximum Likelihood Estimators in Regression Models for Error-prone Group Testing Data

• Published in: Scandinavian journal of statistics Vol. 44; no. 4; pp. 918 - 931
• Main Authors: HUANG, XIANZHENG, WARASI, MD SHAMIM SARKER
• Format: Journal Article
• Language: English
• Published: Oxford Wiley Publishing 01.12.2017; Blackwell Publishing Ltd
• Subjects: Antigens; attenuation; Composition effects; efficiency; Epidemiology; generalized linear model; individual testing; Maximum likelihood estimators; Regression models; Statistical analysis; Statistical inference; Statistical methods; Syphilis
• ISSN: 0303-6898; 1467-9469
• DOI: 10.1111/sjos.12282

Since Dorfman's seminal work on the subject, group testing has been widely adopted in epidemiological studies. In Dorfman's context of detecting syphilis, group testing entails pooling blood samples and testing the pools, as opposed to testing individual samples. A negative pool indicates all individuals in the pool free of syphilis antigen, whereas a positive pool suggests one or more individuals carry the antigen. With covariate information collected, researchers have considered regression models that allow one to estimate covariate-adjusted disease probability. We study maximum likelihood estimators of covariate effects in these regression models when the group testing response is prone to error. We show that, when compared with inference drawn from individual testing data, inference based on group testing data can be more resilient to response misclassification in terms of bias and efficiency. We provide valuable guidance on designing the group composition to alleviate adverse effects of misclassification on statistical inference.