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

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...

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Bibliographic Details
Published in:Scandinavian journal of statistics 2017-12, Vol.44 (4), p.918-931
Main Authors: HUANG, XIANZHENG, WARASI, MD SHAMIM SARKER
Format: Article
Language:English
Subjects:
Antigens
attenuation
Composition effects
efficiency
Epidemiology
generalized linear model
individual testing
Maximum likelihood estimators
Regression models
Statistical analysis
Statistical inference
Statistical methods
Syphilis
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Summary: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.
ISSN:0303-6898
1467-9469
DOI:10.1111/sjos.12282