Several types of statistical methods are currently available for the meta-analysis of studies on diagnostictest accuracy. One of these methods is the Bivariate Model which involves a simultaneous analysisof the sensitivity and specificity from a set of studies.
In this paper, we review the characteristicsof the Bivariate Model and demonstrate how it can be extended with a discrete latent variable. Theresulting 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 insensitivity 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 betweenstudies. Latent classes may not only differ with respect to the average sensitivity and specificity, butalso with respect to the correlation between sensitivity and specificity.
Results:
The Latent Class Bivariate Model identifies clusters of studies with their own estimates of sensitivityand specificity.
Our simulation study demonstrated excellent parameter recovery and good performanceof the model selection statistics typically used in latent class analysis. Application in a realdata example on coronary artery disease showed that the inclusion of latent classes yields interestingadditional information.
Conclusions:
Our proposed new meta-analysis method can lead to a better fit of the data set of interest, less biasedestimates and more reliable confidence intervals for sensitivities and specificities.
But even moreimportant, it may serve as an exploratory tool for subsequent sub-group meta-analyses.
Author: Paolo EusebiJohannes B ReitsmaJeroen K Vermunt
Credits/Source: BMC Medical Research Methodology 2014, 14:88
Published on: 2014-07-11
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