[R-meta] Need to specify meta analysis weights
Emanuele F. Osimo
e|o22 @end|ng |rom c@m@@c@uk
Mon Sep 7 15:38:48 CEST 2020
many thanks for your reply.
A couple of follow-up questions:
1) does using inverse variance to weight studies hold when only few studies
are included, and one is much bigger than the others?
2) why is rma.glmm better than rma.uni which I have used in R to apply
random effect models for ORs? Is there a way to apply rma.glmm using
log(OR) and the standard error of the OR (which is what I have available)
instead of the contingency tables it requires?
Many thanks again for your help.
On Fri, 4 Sep 2020 at 11:55, Gerta Ruecker <ruecker using imbi.uni-freiburg.de>
> Dear Emanuele,
> To your first question: First, Cochrane doesn't recommend weighting by
> N. Cochrane (and others) recommend weighting by inverse variance, and in
> the case of a binary outcome (you mention odds ratios) it is even better
> to use a generalised linear mixed model (GLMM), e.g., logistic
> regression. Also random effect models are available. A random effect
> model is suitable to mitigate the effect of the largest study, or to
> upweight smaller studies, which seems to be desired in your case.
> To the second question: One possibility would be meta-regression with
> length of follow-up as a covariate. Is length of follow-up a study-level
> covariate, or an individual-level covariate? There is no problem in the
> first case, but it may be problematic in the second case, when each
> individual has a different length of follow-up.
> Am 04.09.2020 um 12:22 schrieb Emanuele F. Osimo:
> > Dear all,
> > I am conducting a random-effects meta-analysis of 4 longitudinal studies
> > measuring a blood inflammatory marker earlier on in life, and an
> > outcome (measured in an interview) years later.
> > The studies are not uniform in N (one is about 80k people, the 2 smallest
> > are about 2k people) and in time to follow up (ranging from 8 to 21
> > I have odds ratios and 95% confidence intervals for the outcome based on
> > cut-offs of the baseline marker (e.g. outcome for inflamed vs outcome for
> > non-inflamed).
> > I have 2 questions for you:
> > 1- if I use weighting by N, as recommended by Cochrane, I am basically
> > reporting the findings of the larger study, which gets 88% of the weight.
> > The larger study is possibly qualitatively less good than the 2 smallest
> > studies. What do you suggest to use for weighting? Is there any compound
> > weighting methods that takes into account, say, study quality, N and
> > inverse variance?
> > 2- time to follow-up: even if I am not measuring a difference in outcome
> > over time, but just the risk of an outcome after an exposure, do I need
> > adjust for time to follow-up? And how?
> > Many thanks in advance for your time and thoughts on this.
> > Best wishes,
> > Emanuele
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> Dr. rer. nat. Gerta Rücker, Dipl.-Math.
> Institute of Medical Biometry and Statistics,
> Faculty of Medicine and Medical Center - University of Freiburg
> Stefan-Meier-Str. 26, D-79104 Freiburg, Germany
> Phone: +49/761/203-6673
> Fax: +49/761/203-6680
> Mail: ruecker using imbi.uni-freiburg.de
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