- Thread starter Vlajkony
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- Tags diagnostic test mcmc mcmcglmm meta-analysis

In the attachment, Chapter 3, page 14.

Thanks!

http://www.journalslibrary.nihr.ac.uk/__data/assets/pdf_file/0020/65018/FullReport-hta17010.pdf

Thanks and good luck.

Thanks. From what I know they use true positive, true negative, false positive and false negative results from diagnostic tests as priors (random variables) and then they apply the function to those which as outcomes give sensitivity and specificity. After that, they repeat this using MCMC in 10 000 -15 000 iterations which on the end results in probabilistic results of sensitivity and specificity with credible intervals.

I already have finished modeling with hierarchical SROC (HSROC) model of Rutter and Gatsonis but I want to have the double methodological approach and overcome some pitfalls of Rutter and Gatsonis models with this MCMC approach, especially in the subgroup analysis.

Anyway, I will try with WinBugs technical documentation and try to better understand this approach.

I already have finished modeling with hierarchical SROC (HSROC) model of Rutter and Gatsonis but I want to have the double methodological approach and overcome some pitfalls of Rutter and Gatsonis models with this MCMC approach, especially in the subgroup analysis.

Anyway, I will try with WinBugs technical documentation and try to better understand this approach.

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Metadas v1.3 beta Macro by Takwoingi in SAS 9.4, Cary NC

It appeared to be endorsed by Cochrane Collaborative and provides HSROC. I also seem to remember reading that there are some types of issues with trying to calculate accuracy of diagnostics using meta-analyses and you should focus more on SEN and SPEC, but I can't recall the source or full context (which method it was related to).