The online version of this article (doi:10.1186/1471-2288-14-88) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
PE conceived research questions, developed study design and methods, carried out statistical analysis, interpreted results and drafted the manuscript. JBR advised on the study design, methods, statistical analyses and manuscript. JKV advised on the statistical analyses and software code. All authors commented on successive drafts, and read and approved the final manuscript.
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.
Macaskill P, Gatsonis C, Deeks J, Harbord R, Takwoingi Y: Chapter: Analysing and presenting results. Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Edited by: Deeks J, Bossuyt P, Gatsonis C. 2010, New York: The Cochrane Collaboration, 1-61.
Goodman LA: Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika. 1998, 61 (2): 215-231. CrossRef
Vermunt JK, Magidson J: LG-Syntax user’s guide: Manual for Latent GOLD 4.5 Syntax module. Technical Report. 2008, Belmont, MA: Statistical Innovations
Lin TH, Dayton CM: Model selection information criteria for non-nested latent class models. J Educ Behav Stat. 1997, 22 (3): 249-264. CrossRef
Leeflang MM, Rutjes AW, Reitsma JB, Hooft L, Bossuyt PM: Variation of a test’s sensitivity and specificity with disease prevalence. CMAJ. 2013, 185 (11): 537-544. CrossRef
- Latent class bivariate model for the meta-analysis of diagnostic test accuracy studies
Johannes B Reitsma
Jeroen K Vermunt
- BioMed Central
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