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RoBMA Random-effects meta-analysis (with meta-regression)

Greetings,

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!

Comments

  • I'll forward this to our experts

    Cheers,

    E.J.

  • Hi jber3175,


    1) You can remove any type of models/prior specifications under the Models section. There is a checkbox at the bottom of the section called Set null priors . Selecting the checkbox will open additional settings that allow you to specify models for the null hypotheses. By clicking on the X behind the Spike(x) under the Heterogeneity (null) removes the fixed-effect models from the ensemble (as the heterogeneity parameter tau = 0 correspond to fixed-effect models). Nevertheless, I would advise doing this only if you have a strong justification for it. In a recent paper (under review), we showed that fixed-effect models actually out-predict random-effect models in many cases and constitute an interesting hypothesis. Furthermore, if the data are indeed predicted well by the fixed-effect hypothesis, your test for the presence/absence of the effect and publication bias will become weaker.


    2) Regarding the second question, I will just paste my response from the other thread. Let me know if there was something unclear:

    Currently, the Bayesian Meta-Analysis analysis does not support a metaregression. However, you are correct that you can obtain a similar functionality from the Bayesian Linear Regression analysis - it's important to keep two things in mind though:

    a) You have to use ``WLS Weights'' argument to pass the weighting of the studies (usually 1/se^2). You would discard information about the precision of the study effect size estimate otherwise.

    b) This will result in weighted least squares meta-regression that differs a bit from the fixed/random effects meta-regression models regularly used in psychology. Nevertheless, some authors (e.g., Stanley and Doucouliagos) that WLS meta-regression has better properties than fixed/random effects meta-regressions.


    3) You can use Hedge's g and the corresponding SE as input in the module, however, I'm not sure how does the small study correction interplay with the weighted likelihood. Especially whether it keeps the corresponding p-values cutoffs. I will need to do more research into this.


    Cheers,

    Frantisek

  • Dear Frantisek,

    1. Thank you again for clarifying this. Is it possible to share the pre-print of the paper?

    2. Is the "WLS Weights" argument accessible on JASP or only accessible on R?

    Cheers,

    Josh

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