RoBMA Random-effects meta-analysis (with meta-regression)
I would like to use RoBMA, on JASP (preferably) or R, to conduct a Bayesian random-effects meta-analysis and meta-regression. I have 3 questions which I have asked separately elsewhere (JASP YouTube channel and another user's forum post here), but decided I would also put it together here on the forum for myself.
1. How can I work only with a random-effects model i.e., remove all fixed effects models from the 'Inference' and 'Plots' functions and then get the appropriate (model-averaged) estimates/plots? I am intrigued to know because this was a possibility mentioned on P.18 of Bartoš, Maier, and Wagenmakers' paper "simply removing the fixed effects models from RoBMA".
2. I also wish to explore the possibility of conducting a meta-regression to complement the main RoBMA analysis. I suspect it would be possible and sound to use JASP's Bayesian Linear Regression function for this purpose.
According to Cochrane Handbook for Systematic Reviews of Interventions:
"Meta-regressions are similar in essence to simple regressions, in which an outcome variable is predicted according to the values of one or more explanatory variables."
With a meta-analysis, since we are usually working with and reviewing aggregate data, does a regression analysis turn into a 'meta-regression' by virtue of using these data as part of a systematic review and meta-analysis?
In other words, is it OK to use the Bayesian Linear Regression function in JASP to conduct a Bayesian 'meta-regression' to complement the RoBMA analysis? Ideally with continuous and categorical (dummy-coded) potential effect moderators testing 3 models, e.g., participant characteristics, intervention characteristics, and general study characteristics.
Bergh, D.v.d., Clyde, M.A., Gupta, A.R.K.N. et al. A tutorial on Bayesian multi-model linear regression with BAS and JASP. Behav Res (2021). https://doi.org/10.3758/s13428-021-01552-2
3. How can I use (or plug-in) different point/variance estimators e.g., Hedges' g instead of the default Cohen's d (and apply the corresponding name/label to the figures)? For my analysis, I need to work with the standardized mean difference, but should the need arise, it would be great if I could use alternative estimators like Hedges' g to account for potentially small studies. Any advice on how to implement this on JASP/R would be appreciated.
I am new to the field and area of meta-analysis, so pardon me for my superficial understanding and if I do not understand whether there is a genuine difference between a meta-regression and an 'ordinary' regression for Bayesian analysis.
Thank you in advance!