Bayesian latent class models for identifying canine visceral leishmaniosis using diagnostic tests in the absence of a gold standard.

ABSTRACT:

Background

Like many infectious diseases, there is no practical gold standard for diagnosing clinical visceral leishmaniasis (VL). Latent class modeling has been proposed to estimate a latent gold standard for identifying disease. These proposed models for VL have leveraged information from diagnostic tests with dichotomous serological and PCR assays, but have not employed continuous diagnostic test information.

Methods/principal findings

In this paper, we employ Bayesian latent class models to improve the identification of canine visceral leishmaniasis using the dichotomous PCR assay and the Dual Path Platform (DPP) serology test. The DPP test has historically been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Using data collected

SUBMITTER: Ozanne MV 

PROVIDER: S-EPMC8947804 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature