Bayesian Latent Class Models in malaria diagnosis.

ABSTRACT:

Aims

The main focus of this study is to illustrate the importance of the statistical analysis in the evaluation of the accuracy of malaria diagnostic tests, without admitting a reference test, exploring a dataset (n=3317) collected in São Tomé and Príncipe.

Methods

Bayesian Latent Class Models (without and with constraints) are used to estimate the malaria infection prevalence, together with sensitivities, specificities, and predictive values of three diagnostic tests (RDT, Microscopy and PCR), in four subpopulations simultaneously based on a stratified analysis by age groups (< 5, ≥ 5 years old) and fever status (febrile, afebrile).

Results

In the afebrile individuals with at least five years old, the posterior mean of the malaria infection prevalence is 3.2% with a

SUBMITTER: Goncalves L 

PROVIDER: S-EPMC3402519 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature