# Which is the best method for meta-analysis of diagnostic test accuracy studies?

I am conducting a meta-analysis of diagnostic test accuracy studies focusing on myocardial perfusion imaging. I have used first Meta-Disc, but only for descriptive purposes, as it is clear that univariate approaches such as those provided by this package are biased (eg Takwoingi et al). Then, I have found the following bivariate methods, and used several of them:

1. Bayesian bivariate model using bamdit in R;
2. Bayesian bivariate model using meta4diag in R;
3. Bayesian HSROC using HSROC in R;
4. frequentist bivariate model using metamisc in R;
5. frequentist bivariate model using metandi in Stata;
6. frequentist copula mixed model using CopulaREMADA in R;
7. frequentist hierarchical summary receiver operating characteristic (HSROC) model using metandi in Stata;
8. frequentist proportional hazard model using mada in R;
9. frequentist Reitsma model using mada in R;
10. frequentist Reitsma model using Metatron in R.

Results are similar across many of these methods, albeit obviously not identical. Yet, I would favor the Reitsma model as available in mada as it gives me more comprehensive analytical and graphical results.

My questions stem from this actual project but are quite more general.

Is there a method which is best for meta-analysis of diagnostic test accuracy studies? Or are they more or less similar? Is there any other method not listed above which is better still?