Dear Dr. Viechtbauer and all, Thank you Dr. Viechtbauer for answering my previous question on multilevel meta-analysis. It is detailed and helped me a lot. I ran into other problems when I did the publication bias analysis. As we all know, regtest() and trimfill() have not been implemented for rma.mv objects. But I know to implement Egger's regression test, we can simply enter the standard error as a moderator to the rma.mv() function. For example: R> rma.mv(yi, vi, mods = Standard_Error, random = ~1 | Study/Outcome) Yet, you have mentioned in another thread in 2017 that trim and fill had not been extended to this kind of complex model and we should not do the trim and fill on the rma.mv object. However, I still read some papers that would like to do the trim and fill even when their primary analysis was a multilevel model. For example, in Kredlow and colleagues' (2016) study, they mentioned that "(For publication bias analysis,) to examine the full pattern of effects, a multilevel approach was not used and these analyses were conducted in Comprehensive Meta-Analysis with each effect treated as independent. (p.318)". Precisely, their main approach was the multilevel model, but they did the publication bias analysis by re-entering the data into a simple model. What do you think about this approach? Can someone run a simple random effect model to a dataset, which originally was fit into a multilevel model, to conduct the publication bias analysis (specifically the regression test and trim and fill method)? Are this analysis still meaningful to the results of the original multilevel model? Regards, Man Hey References: Kredlow, M. A., Unger, L. D., & Otto, M. W. (2016). Harnessing reconsolidation to weaken fear and appetitive memories: A meta-analysis of post-retrieval extinction effects. *Psychological Bulletin*, *142*(3), 314.
[R-meta] Analysis of publication bias on rma.mv object
2 messages · CHIU, Man Hey, Wolfgang Viechtbauer
6 days later
Dear Man Hey, If the data have a multilevel structure, then I think one should analyze them as such, whether one is conducting the 'main' analysis or some kind of publication bias analysis. Best, Wolfgang -----Original Message----- From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of CHIU, Man Hey Sent: Wednesday, 18 July, 2018 8:30 To: r-sig-meta-analysis at r-project.org Subject: [R-meta] Analysis of publication bias on rma.mv object Dear Dr. Viechtbauer and all, Thank you Dr. Viechtbauer for answering my previous question on multilevel meta-analysis. It is detailed and helped me a lot. I ran into other problems when I did the publication bias analysis. As we all know, regtest() and trimfill() have not been implemented for rma.mv objects. But I know to implement Egger's regression test, we can simply enter the standard error as a moderator to the rma.mv() function. For example: R> rma.mv(yi, vi, mods = Standard_Error, random = ~1 | Study/Outcome) Yet, you have mentioned in another thread in 2017 that trim and fill had not been extended to this kind of complex model and we should not do the trim and fill on the rma.mv object. However, I still read some papers that would like to do the trim and fill even when their primary analysis was a multilevel model. For example, in Kredlow and colleagues' (2016) study, they mentioned that "(For publication bias analysis,) to examine the full pattern of effects, a multilevel approach was not used and these analyses were conducted in Comprehensive Meta-Analysis with each effect treated as independent. (p.318)". Precisely, their main approach was the multilevel model, but they did the publication bias analysis by re-entering the data into a simple model. What do you think about this approach? Can someone run a simple random effect model to a dataset, which originally was fit into a multilevel model, to conduct the publication bias analysis (specifically the regression test and trim and fill method)? Are this analysis still meaningful to the results of the original multilevel model? Regards, Man Hey References: Kredlow, M. A., Unger, L. D., & Otto, M. W. (2016). Harnessing reconsolidation to weaken fear and appetitive memories: A meta-analysis of post-retrieval extinction effects. *Psychological Bulletin*, *142*(3), 314.