[R-meta] random effects MA with correlations

Viechtbauer, Wolfgang (SP) wolfg@ng@viechtb@uer @ending from m@@@trichtuniver@ity@nl
Thu Sep 13 10:09:21 CEST 2018


Dear F.,

It is impossible to say which estimator (REML, DL, PM, etc.) is optimal in any particular case (if, by optimal, we mean: 'closest to the true value').

We also cannot say that one of these estimators is better on average (i.e., in the sense of being a UMVUE -- https://en.wikipedia.org/wiki/Minimum-variance_unbiased_estimator). In some circumstances, one is better, while under other circumstances, another is better.

A lot has been written about 'tau^2' estimators. These days, I feel like this is one of the least important issues in a meta-analysis. There are exceptions where it matters, but in most cases, conclusions won't change depending on the estimator. I personally prefer ML/REML because these methods automatically can be generalized to more complex models, while other estimators do not.

As for the Hartung-Knapp method: Use it. It really should be the default (too late to change this now in metafor, but I should have done this from the beginning).

Not sure what other arguments you are concerned about, but you probably do not have to mess with them.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of F.Anagnostopoulos
Sent: Thursday, 13 September, 2018 8:59
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] random effects MA with correlations

Dear all,

Regarding  the  use of  proper procedures to estimate between-study variance/heterogeneity  (tau-squared) and its CIs, when performing random effects meta-analysis with correlation coefficients as effect sizes, using R, the "metafor" package implements REML by default when estimating tau-squared, while "metacor" uses DSL by default for examining random effects with correlation coefficients. Others suggest the Paule- Mandel method.

Thus, which is the optimal method in this case, especially when the number of studies= 20, the mean sample size=250, average r = 0.525 (95% CI= 0.411- 0.622), tau-squared= 0.10, I-squared= 98%, and correlations are converted  to Fisher's  z values? . 
Moreover, is it necessary  to apply the Hartung- Knapp method  to adjust test statistics and CIs? Should we  pay special attention to any "arguments" used in "metafor" package, when dealing with correlations?
Thanks in advance.

F. Agnew



More information about the R-sig-meta-analysis mailing list