Regression analysis for multiple‐disease group testing data

Abstract

Group testing, where individual specimens are composited into groups to test for the presence of a disease (or other binary characteristic), is a procedure commonly used to reduce the costs of screening a large number of individuals. Group testing data are unique in that only group responses may be available, but inferences are needed at the individual level. A further methodological challenge arises when individuals are tested in groups for multiple diseases simultaneously, because unobserved individual disease statuses are likely correlated. In this paper, we propose new regression techniques for multiple-disease group testing data. We develop an expectation-solution based algorithm that provides consistent parameter estimates and natural large-sample inference procedures. Our proposed methodology is applied to chlamydia and gonorrhea screening data collected in Nebraska as part of the Infertility Prevention Project and to prenatal infectious disease screening data from Kenya.

Medical informatics research trend analysis: A text mining approach

“…Statistical methods applied to medical informatics research are Bayesian strategies for monitoring clinical trial data, Bayesian analysis of multicenter trial outcomes, Bayesian approach to phase I cancer trials, techniques for incorporating longitudinal measurements into analyses of survival data from clinical trials, estimation and testing based on data subject to measurement errors, regression analysis for multiple-disease group testing data, power and sample size calculation for log-rank testing with a time lag in treatment effect, joint modeling of multivariate longitudinal measurements and survival data with applications to Parkinson’s disease, and regression analysis for the examination of the benefits of group testing. 62–69…”

Positing, fitting, and selecting regression models for pooled biomarker data

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Regression analysis and variable selection for two‐stage multiple‐infection group testing data

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