[R-meta] Advice on whether to add nested study group effects to longitudinal meta analysis
M@G|rdwood @end|ng |rom |@trobe@edu@@u
Mon Jan 23 02:16:29 CET 2023
Thank you again for all the valuable information on this message board.
I am planning a longitudinal meta-analysis and am looking for advice on the best approach for my data structure. This is an example for one of the outcomes we are investigating. We currently have k=120 studies measuring this outcome with a variety of different timepoints and follow ups (i.e. some cross-sectional, some longitudinal). I have spent a lot of time reading about the different ways to set up a so called longitudinal MA including different correlation structures etc, so for now I am ok there. My questions is around adding additional levels to the analysis.
Quite a few of the studies (71) use multiple independent groups (but same population of interest for us), just split slightly differently across studies. The way these have been split is of less interest to us / the outcome, and the grouping is not consistent between studies. (I.e. group A in study 1 is not the same grouping as group Ain study 2)
## Do you think it would be better to keep these groups separate and nest their effects within studies? My hesitancy doing this/reason for seeking advice about this is whether this will add too much complexity, and make it difficult to distinguish between different levels of variance
Confirming then to specify my random effects I would use
random = list(~study | group), ~ time | interaction(study, group)
## Alternatively should I combine these groups into one before running the meta-analysis, to simplify the structure (so one effect per study)? Obviously I’m aware of the downsides and cautions of such data reduction too
I appreciate there is no black and white best answer, and answer is very context dependant, but your advice is greatly appreciated! For reference these two previous questions were helpful and similar in nature https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2021-April/002794.html, https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-July/000896.html. Also of note is that unfortunately as is unsurprising we don’t have exact data on correlation between timepoints or correlation within study groups, so we will have to estimate these to construct approx covariance matrices. To help overcome these limitations I’m planning on using clubSandwhich and robust methods as appropriate.
La Trobe University | Australia
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