Latent class bivariate model for the meta-analysis of diagnostic test accuracy studies

P. Eusebi, J.B. Reitsma, J.K. Vermunt

Research output: Contribution to journalArticleScientificpeer-review

31 Downloads (Pure)

Abstract

Background
Several types of statistical methods are currently available for the meta-analysis of studies on diagnostic test accuracy. One of these methods is the Bivariate Model which involves a simultaneous analysis of the sensitivity and specificity from a set of studies. In this paper, we review the characteristics of the Bivariate Model and demonstrate how it can be extended with a discrete latent variable. The resulting clustering of studies yields additional insight into the accuracy of the test of interest.
Methods
A Latent Class Bivariate Model is proposed. This model captures the between-study variability in sensitivity and specificity by assuming that studies belong to one of a small number of latent classes. This yields both an easier to interpret and a more precise description of the heterogeneity between studies. Latent classes may not only differ with respect to the average sensitivity and specificity, but also with respect to the correlation between sensitivity and specificity.
Results
The Latent Class Bivariate Model identifies clusters of studies with their own estimates of sensitivity and specificity. Our simulation study demonstrated excellent parameter recovery and good performance of the model selection statistics typically used in latent class analysis. Application in a real data example on coronary artery disease showed that the inclusion of latent classes yields interesting additional information.
Conclusions
Our proposed new meta-analysis method can lead to a better fit of the data set of interest, less biased estimates and more reliable confidence intervals for sensitivities and specificities. But even more important, it may serve as an exploratory tool for subsequent sub-group meta-analyses.
Keywords: Meta-analysis, Meta-regression, Bivariate model, Latent class model
Original languageEnglish
Article number88
JournalBMC Medical Research Methodology
Volume14
DOIs
Publication statusPublished - 2014

Fingerprint

Routine Diagnostic Tests
Cluster Analysis
Confidence Intervals

Cite this

@article{b38abdbd6d3345b98fb893a0a3562a11,
title = "Latent class bivariate model for the meta-analysis of diagnostic test accuracy studies",
abstract = "BackgroundSeveral types of statistical methods are currently available for the meta-analysis of studies on diagnostic test accuracy. One of these methods is the Bivariate Model which involves a simultaneous analysis of the sensitivity and specificity from a set of studies. In this paper, we review the characteristics of the Bivariate Model and demonstrate how it can be extended with a discrete latent variable. The resulting clustering of studies yields additional insight into the accuracy of the test of interest.MethodsA Latent Class Bivariate Model is proposed. This model captures the between-study variability in sensitivity and specificity by assuming that studies belong to one of a small number of latent classes. This yields both an easier to interpret and a more precise description of the heterogeneity between studies. Latent classes may not only differ with respect to the average sensitivity and specificity, but also with respect to the correlation between sensitivity and specificity.ResultsThe Latent Class Bivariate Model identifies clusters of studies with their own estimates of sensitivity and specificity. Our simulation study demonstrated excellent parameter recovery and good performance of the model selection statistics typically used in latent class analysis. Application in a real data example on coronary artery disease showed that the inclusion of latent classes yields interesting additional information.ConclusionsOur proposed new meta-analysis method can lead to a better fit of the data set of interest, less biased estimates and more reliable confidence intervals for sensitivities and specificities. But even more important, it may serve as an exploratory tool for subsequent sub-group meta-analyses.Keywords: Meta-analysis, Meta-regression, Bivariate model, Latent class model",
author = "P. Eusebi and J.B. Reitsma and J.K. Vermunt",
year = "2014",
doi = "10.1186/1471-2288-14-88",
language = "English",
volume = "14",
journal = "BMC Medical Research Methodology",
issn = "1471-2288",
publisher = "BioMed Central",

}

Latent class bivariate model for the meta-analysis of diagnostic test accuracy studies. / Eusebi, P.; Reitsma, J.B.; Vermunt, J.K.

In: BMC Medical Research Methodology, Vol. 14, 88, 2014.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Latent class bivariate model for the meta-analysis of diagnostic test accuracy studies

AU - Eusebi, P.

AU - Reitsma, J.B.

AU - Vermunt, J.K.

PY - 2014

Y1 - 2014

N2 - BackgroundSeveral types of statistical methods are currently available for the meta-analysis of studies on diagnostic test accuracy. One of these methods is the Bivariate Model which involves a simultaneous analysis of the sensitivity and specificity from a set of studies. In this paper, we review the characteristics of the Bivariate Model and demonstrate how it can be extended with a discrete latent variable. The resulting clustering of studies yields additional insight into the accuracy of the test of interest.MethodsA Latent Class Bivariate Model is proposed. This model captures the between-study variability in sensitivity and specificity by assuming that studies belong to one of a small number of latent classes. This yields both an easier to interpret and a more precise description of the heterogeneity between studies. Latent classes may not only differ with respect to the average sensitivity and specificity, but also with respect to the correlation between sensitivity and specificity.ResultsThe Latent Class Bivariate Model identifies clusters of studies with their own estimates of sensitivity and specificity. Our simulation study demonstrated excellent parameter recovery and good performance of the model selection statistics typically used in latent class analysis. Application in a real data example on coronary artery disease showed that the inclusion of latent classes yields interesting additional information.ConclusionsOur proposed new meta-analysis method can lead to a better fit of the data set of interest, less biased estimates and more reliable confidence intervals for sensitivities and specificities. But even more important, it may serve as an exploratory tool for subsequent sub-group meta-analyses.Keywords: Meta-analysis, Meta-regression, Bivariate model, Latent class model

AB - BackgroundSeveral types of statistical methods are currently available for the meta-analysis of studies on diagnostic test accuracy. One of these methods is the Bivariate Model which involves a simultaneous analysis of the sensitivity and specificity from a set of studies. In this paper, we review the characteristics of the Bivariate Model and demonstrate how it can be extended with a discrete latent variable. The resulting clustering of studies yields additional insight into the accuracy of the test of interest.MethodsA Latent Class Bivariate Model is proposed. This model captures the between-study variability in sensitivity and specificity by assuming that studies belong to one of a small number of latent classes. This yields both an easier to interpret and a more precise description of the heterogeneity between studies. Latent classes may not only differ with respect to the average sensitivity and specificity, but also with respect to the correlation between sensitivity and specificity.ResultsThe Latent Class Bivariate Model identifies clusters of studies with their own estimates of sensitivity and specificity. Our simulation study demonstrated excellent parameter recovery and good performance of the model selection statistics typically used in latent class analysis. Application in a real data example on coronary artery disease showed that the inclusion of latent classes yields interesting additional information.ConclusionsOur proposed new meta-analysis method can lead to a better fit of the data set of interest, less biased estimates and more reliable confidence intervals for sensitivities and specificities. But even more important, it may serve as an exploratory tool for subsequent sub-group meta-analyses.Keywords: Meta-analysis, Meta-regression, Bivariate model, Latent class model

U2 - 10.1186/1471-2288-14-88

DO - 10.1186/1471-2288-14-88

M3 - Article

VL - 14

JO - BMC Medical Research Methodology

JF - BMC Medical Research Methodology

SN - 1471-2288

M1 - 88

ER -