A Bayesian approach to model the conditional correlation between several diagnostic tests and various replicated subjects measurements

Two key aims of diagnostic research are to accurately and precisely estimate disease prevalence and test sensitivity and specificity. Latent class models have been proposed that consider the correlation between subject measures determined by different tests in order to diagnose diseases for which go...

Full description

Saved in:
Bibliographic Details
Published in:Statistics in medicine 2017-09, Vol.36 (20), p.3154-3170
Main Authors: Pereira da Silva, Hélio Doyle, Ascaso, Carlos, Gonçalves, Alessandra Queiroga, Orlandi, Patricia Puccinelli, Abellana, Rosa
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Bayesian approach
Bias
Biostatistics
Brazil - epidemiology
Computer Simulation
Cross-Sectional Studies - statistics & numerical data
diagnostic test
Diagnostic tests
Diagnostic Tests, Routine - statistics & numerical data
Epidemiology
Hookworm Infections - diagnosis
Hookworm Infections - epidemiology
Humans
latent class model
Likelihood Functions
Medical statistics
Models, Statistical
Prevalence
replicated measurement
sensitivity
specificity
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Two key aims of diagnostic research are to accurately and precisely estimate disease prevalence and test sensitivity and specificity. Latent class models have been proposed that consider the correlation between subject measures determined by different tests in order to diagnose diseases for which gold standard tests are not available. In some clinical studies, several measures of the same subject are made with the same test under the same conditions (replicated measurements), and thus, replicated measurements for each subject are not independent. In the present study, we propose an extension of the Bayesian latent class Gaussian random effects model to fit the data with binary outcomes for tests with replicated subject measures. We describe an application using data collected on hookworm infection carried out in the municipality of Presidente Figueiredo, Amazonas State, Brazil. In addition, the performance of the proposed model was compared with that of current models (the subject random effects model and the conditional (in)dependent model) through a simulation study. As expected, the proposed model presented better accuracy and precision in the estimations of prevalence, sensitivity and specificity. Copyright © 2017 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.7339