Bayesian bivariate meta‐analysis of diagnostic test studies using integrated nested Laplace approximations

Paul, Manabi; Riebler, Andrea; Bachmann, Lucas M.; Rue, Håvard; Held, Leonhard

doi:10.1002/sim.3858

Cited by 77 publications

(115 citation statements)

References 27 publications

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“…The above framework is an example of a generalized linear mixed model (GLMM) and we used a Bayesian approach to inference with relatively flat hyperpriors. One computationally intensive method for summarizing the posterior would be Markov chain Monte Carlo (MCMC) but the integrated nested Laplace approximation (INLA) as described in [57] provides an efficient alternative for GLMMs [58]. We used the R implementation of INLA to estimate Δ.…”

Section: Methodsmentioning

confidence: 99%

Evolution and Genetic Architecture of Chromatin Accessibility and Function in Yeast

2014

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Chromatin accessibility is an important functional genomics phenotype that influences transcription factor binding and gene expression. Genome-scale technologies allow chromatin accessibility to be mapped with high-resolution, facilitating detailed analyses into the genetic architecture and evolution of chromatin structure within and between species. We performed Formaldehyde-Assisted Isolation of Regulatory Elements sequencing (FAIRE-Seq) to map chromatin accessibility in two parental haploid yeast species, Saccharomyces cerevisiae and Saccharomyces paradoxus and their diploid hybrid. We show that although broad-scale characteristics of the chromatin landscape are well conserved between these species, accessibility is significantly different for 947 regions upstream of genes that are enriched for GO terms such as intracellular transport and protein localization exhibit. We also develop new statistical methods to investigate the genetic architecture of variation in chromatin accessibility between species, and find that cis effects are more common and of greater magnitude than trans effects. Interestingly, we find that cis and trans effects at individual genes are often negatively correlated, suggesting widespread compensatory evolution to stabilize levels of chromatin accessibility. Finally, we demonstrate that the relationship between chromatin accessibility and gene expression levels is complex, and a significant proportion of differences in chromatin accessibility might be functionally benign.

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Section: Methodsmentioning

confidence: 99%

Evolution and Genetic Architecture of Chromatin Accessibility and Function in Yeast

2014

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“…For this reason, INLA can be successfully used in a great variety of applications (e.g. Li et al, 2012;Riebler et al, 2012;Ruiz-Cárdenas et al, 2012;Martino et al, 2011;Roos and Held, 2011;Schrö-dle and Held, 2011a,b;Schrödle et al, 2011;Paul et al, 2010), also thanks to the availability of an R package named R-INLA (Martino and Rue, 2010). Furthermore, INLA can be combined with the Stochastic Partial Differential Equation (SPDE) approach proposed by Lindgren et al (2011) in order to implement spatial and spatio-temporal models for point-reference data.…”

Section: Introductionmentioning

confidence: 99%

Spatial and spatio-temporal models with R-INLA

Blangiardo

Cameletti

Baio

et al. 2013

Spatial and Spatio-temporal Epidemiology

413

426

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During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint.Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method.In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.

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“…In order to avoid the Markov chain Monte Carlo sampling also a deterministic Bayesian approach using integrated nested Laplace approximations have been proposed [22]. BM can be seen within a unified framework which includes also the Hierarchical Summary ROC model [23].…”

Section: Introductionmentioning

confidence: 99%

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

Reitsma

Vermunt

2014

BMC Med Res Methodol

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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.

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Bayesian bivariate meta‐analysis of diagnostic test studies using integrated nested Laplace approximations

Cited by 77 publications

References 27 publications

Evolution and Genetic Architecture of Chromatin Accessibility and Function in Yeast

Evolution and Genetic Architecture of Chromatin Accessibility and Function in Yeast

Spatial and spatio-temporal models with R-INLA

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

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