Latent class bivariate model for the meta-analysis of diagnostic test accuracy studies
1 Department of Epidemiology, Regional Health Authority of Umbria, Via Mario Angeloni, 61, 06124 Perugia, Italy
2 Neurologic Clinic, Department of Medicine, University of Perugia, Perugia, Italy
3 Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
4 Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands
BMC Medical Research Methodology 2014, 14:88 doi:10.1186/1471-2288-14-88Published: 11 July 2014
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.
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.
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.
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.