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...
| Publicado en: | Statistics in Medicine Vol. 36; no. 20; pp. 3154 - 3171 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
9/10/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124433069&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124433069 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 9/10/2017 vid: 36 iid: 20 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 124433069 124433069 NLM28543307 10.1002/sim.7339 NLM28543307 124433069 ppf: 3154 ppct: 17 formats: tig: atl: A Bayesian approach to model the conditional correlation between several diagnostic tests and various replicated subjects measurements. aug: au: Pereira da Silva, Hélio Doyle Ascaso, Carlos Gonçalves, Alessandra Queiroga Orlandi, Patricia Puccinelli Abellana, Rosa affil: Biostatistics Unit, Public Health Department, University of Barcelona, Barcelona Spain sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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