Med Decis Making - Bivariate random-effects meta-analysis of sensitivity and specificity with the Bayesian SAS PROC MCMC: methodology and empirical evaluation in 50 meta-analyses.

Tópicos

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Resumo

CKGROUND AND OBJECTIVE: Meta-analysis allows for summarizing the sensitivities and specificities from several primary diagnostic test accuracy studies quantitatively. This article presents and evaluates a full Bayesian method for bivariate random-effects meta-analysis of sensitivity and specificity with SAS PROC MCMC.METHODS: First, the formula of the bivariate random-effects model is presented. Then its implementation with the Bayesian SAS PROC MCMC is empirically evaluated, using the published 2 ? 2 count data of 50 meta-analyses. The convergence of the Markov chains is analyzed visually and qualitatively. The results are compared with a Bayesian WinBUGS approach, using the Bland-Altman analysis for assessing agreement between 2 methods.RESULTS: The 50 meta-analyses covered broad ranges of pooled sensitivity (17.4% to 98.8%) and specificity (60.0% to 99.7%), and the between-study heterogeneity varied as well. In all meta-analyses, the Markov chains converged well. The meta-analytic results from the SAS PROC MCMC and the WinBUGS random-effects approaches were nearly similar, showing close 95% limits of agreement for the pooled sensitivity (-0.06% to 0.05%) and specificity (-0.05% to 0.05%) without significant differences (P > 0.05). This indicates that the bivariate model is well implemented with both different statistical programs, without systematic differences arising from program attributes.CONCLUSIONS: As alternative to a WinBUGS approach, the Bayesian SAS PROC MCMC is well suited for bivariate random-effects meta-analysis of sensitivity and specificity.

Resumo Limpo

ckground object metaanalysi allow summar sensit specif sever primari diagnost test accuraci studi quantit articl present evalu full bayesian method bivari randomeffect metaanalysi sensit specif sas proc mcmcmethod first formula bivari randomeffect model present implement bayesian sas proc mcmc empir evalu use publish count data metaanalys converg markov chain analyz visual qualit result compar bayesian winbug approach use blandaltman analysi assess agreement methodsresult metaanalys cover broad rang pool sensit specif betweenstudi heterogen vari well metaanalys markov chain converg well metaanalyt result sas proc mcmc winbug randomeffect approach near similar show close limit agreement pool sensit specif without signific differ p indic bivari model well implement differ statist program without systemat differ aris program attributesconclus altern winbug approach bayesian sas proc mcmc well suit bivari randomeffect metaanalysi sensit specif

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