[R-meta] moderator and adjusted effects
Dear Valeria, another difference between your options (1) and (2) is that in case of (1) you get three different, independent estimates of the heterogeneity (tau), whereas in (2) you assume a common heterogeneity parameter for all three groups. In case you have "many" studies in each group (say, 20), it may not make much of a difference, but if you have "few" studies (say, 5) in some, and the assumption of a common heterogeneity parameter seems plausible, then borrowing information on the heterogeneity across the three groups may help. Cheers, Christian
On Mon, 2020-12-14 at 18:18 +0000, Michael Dewey wrote:
Dear Valeria Comments in-line On 14/12/2020 17:13, Valeria Ivaniushina wrote:
Dear experts, In my sample of articles for meta-analysis there are three categories, or three conditions, that may influence the effect of interest. I am more interested in estimating different effects from these conditions than in explaining heterogeneity in effect sizes. 1) I can do a meta-analysis for each of these conditions separately and get three different mean effect sizes. 2) Or I can do a meta-analysis of the whole sample, then include a condition as a moderator and calculate adjusted effects as described here :
http://www.metafor-project.org/doku.php/tips:computing_adjusted_effects
I would go for option two as it will give you estimates of the differences between the levels of your moderator.
Which option is better? Additional question: when I include a categorical moderator, is it the same as including a dummy variable in a regression? How can I specify that the variable is categorical with 3 levels?
If you make the moderator a factor then R will take care of this for you.
Best, Valeria [[alternative HTML version deleted]]
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