Bayesian latent class analysis produced diagnostic accuracy estimates that were more interpretable than composite reference standards for extrapulmonary tuberculosis tests.

ABSTRACT:

Background

Evaluating the accuracy of extrapulmonary tuberculosis (TB) tests is challenging due to lack of a gold standard. Latent class analysis (LCA), a statistical modeling approach, can adjust for reference tests' imperfect accuracies to produce less biased test accuracy estimates than those produced by commonly used methods like composite reference standards (CRSs). Our objective is to illustrate how Bayesian LCA can address the problem of an unavailable gold standard and demonstrate how it compares to using CRSs for extrapulmonary TB tests.

Methods

We re-analyzed a dataset of presumptive extrapulmonary TB cases in New Delhi, India, for three forms of extrapulmonary TB. Results were available for culture, smear microscopy, Xpert MTB/RIF, and a non-microbiological test,

SUBMITTER: MacLean EL 

PROVIDER: S-EPMC9202094 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature