[R-meta] Dear Wolfgang
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Mon Mar 30 12:37:59 CEST 2020
Before I can try to address your actual questions, please say a bit more about the studies that measure responses at a single time point. Are groups (e.g., treatment versus control) being compared within these studies? Are the 'responses' continuous (such that means and SDs are being reported) or dichotomous (such that counts or proportions are being reported) or something else? And related to this, what effect size measure are you using for quantifying the group difference within studies? Standardized mean differences (which would make sense when means/SDs are being reported), risk differences or (log) risk/odds ratios (based on counts/proportions), or something else?
And the studies that measure responses at multiple time points: Are they just doing the same thing that the 'single time point studies' are doing, but at multiple time points? For example, instead of reporting the means and SDs of the treatment and control group once, there are several follow-ups, such that such the means and SDs of the two groups are reported at each follow-up time point?
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Ju Lee
Sent: Sunday, 29 March, 2020 20:16
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] Dear Wolfgang
I sincerely hope you are well and healthy.
I wanted to reach out regarding ways to incorporate studies with repeated-measures to overall mixed effect models.
My data is almost entirely composed of studies measuring responses at a single time point, but there are few studies that have been measuring responses multiple times throughout study seasons. I was advised that time-averaging these multiple responses makes more sense for these studies.
My understanding was that you could 1) do a fixed effect meta-analysis of these studies to generate a single mean effect sizes and sampling variance from these repeated measurements and then 2) incorporate the single effect size and variance into overall mixed-effect model. Is this a correct approach?
If so, how would I calculate sampling variance from the fixed model in the step 1? Is it based on SE outputs of the fixed effect model?
Thank you very much, and I look forward to hearing from you!
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