Informative Retesting

• Published in: Journal of the American Statistical Association Vol. 105; no. 491; pp. 942 - 955
• Main Authors: Bilder, Christopher R., Tebbs, Joshua M., Chen, Peng
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01.09.2010; American Statistical Association; Assoc; Taylor & Francis Ltd
• Subjects: Algorithms; Applications; Applications and Case Studies; Binary response; Chlamydia; Chlamydia trachomatis; Cumulative distribution functions; Data analysis; Discovery; Disease models; Diseases; Exact sciences and technology; General topics; Genetic screening; Gonorrhea; Group size; Group testing; Health; Heterogeneity; Identification; Infectious diseases; Infertility; Information; Mathematics; Medical screening; Medical tests; Pooled testing; Prevention programs; Probabilities; Probability; Probability and statistics; Public health; Regression analysis; Sciences and techniques of general use; Screening tests; Sexually transmitted diseases; Specimens; Statistical data; Statistical methods; Statistics; STD; Surveys; Test design; Tests; Urine; United States > US; Nebraska; Drug; Probability; Probability distribution; Identification; Gonorrhea; Covariate; Algorithm; Implementation; Statistical method; Prevention; Heterogeneity; Statistical test; Infectious diseases; Chlamydia; Group testing; Binary response; Group test; Genetics; Pooled testing; Public health
• ISSN: 0162-1459; 1537-274X; 1537-274X
• DOI: 10.1198/jasa.2010.ap09231

In situations where individuals are screened for an infectious disease or other binary characteristic and where resources for testing are limited, group testing can offer substantial benefits. Group testing, where subjects are tested in groups (pools) initially, has been successfully applied to problems in blood bank screening, public health, drug discovery, genetics, and many other areas. In these applications, often the goal is to identify each individual as positive or negative using initial group tests and subsequent retests of individuals within positive groups. Many group testing identification procedures have been proposed; however, the vast majority of them fail to incorporate heterogeneity among the individuals being screened. In this paper, we present a new approach to identify positive individuals when covariate information is available on each. This covariate information is used to structure how retesting is implemented within positive groups; therefore, we call this new approach "informative retesting." We derive closed-form expressions and implementation algorithms for the probability mass functions for the number of tests needed to decode positive groups. These informative retesting procedures are illustrated through a number of examples and are applied to chlamydia and gonorrhea testing in Nebraska for the Infertility Prevention Project. Overall, our work shows compelling evidence that informative retesting can dramatically decrease the number of tests while providing accuracy similar to established noninformative retesting procedures. This article has supplementary material online.