Bayesian Additive Regression Trees for Group Testing Data.

ABSTRACT: When screening for low-prevalence diseases, pooling specimens (e.g., blood, urine, swabs, etc.) through group testing has the potential to substantially reduce costs when compared to testing specimens individually. A common goal in group testing applications is to estimate the relationship between an individual's true disease status and their individual-level covariate information. However, estimating such a relationship is a non-trivial problem because true individual disease statuses are unknown due to the group testing protocol and the possibility of imperfect testing. While several regression methods have been developed in recent years to accommodate the complexity of group testing data, the functional form of covariate effects is typically assumed to be known. To avoid model misspecif

SUBMITTER: St Ville ME 

PROVIDER: S-EPMC11907685 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature