Res Synth Methods - Bayesian model selection for meta-analysis of diagnostic test accuracy data: Application to Ddimer for deep vein thrombosis.

Tópicos

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Resumo

A number of statistical models have been developed for meta-analysis (MA) of diagnostic test (DT) accuracy data. Here we consider these alternative MA models, explore the relationships between them, and consider the use of the deviance information criteria (DIC) to decide which is the most appropriate model for a given dataset. A Bayesian statistical approach is adopted throughout. The alternative MA models are applied to a dataset of 198 assays of Ddimer to diagnose deep vein thrombosis. In this example, based on the DIC, a bivariate random effects model for sensitivity and specificity fitted the data best. When considering the inclusion of study level covariates, allowing sensitivity to vary by study setting further improved the fit of the model. The model fitting approach is then repeated for a subset of the data, which highlights the less decisive results obtained when using the DIC with a more limited dataset. Formal approaches to model selection are often overlooked in an MA context; however, they offer sensible rationale to the analysis, particularly for complex models such as those proposed for DT accuracy. Specifically, the use of the DIC statistic appears to be well suited for deciding between potentially complex mixed-effect MA models, possibly including covariates. Copyright ? 2010 John Wiley & Sons, Ltd.

Resumo Limpo

number statist model develop metaanalysi ma diagnost test dt accuraci data consid altern ma model explor relationship consid use devianc inform criteria dic decid appropri model given dataset bayesian statist approach adopt throughout altern ma model appli dataset assay ddimer diagnos deep vein thrombosi exampl base dic bivari random effect model sensit specif fit data best consid inclus studi level covari allow sensit vari studi set improv fit model model fit approach repeat subset data highlight less decis result obtain use dic limit dataset formal approach model select often overlook ma context howev offer sensibl rational analysi particular complex model propos dt accuraci specif use dic statist appear well suit decid potenti complex mixedeffect ma model possibl includ covari copyright john wiley son ltd

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