[R-meta] Need to specify meta analysis weights
Gerta Ruecker
ruecker @end|ng |rom |mb|@un|-|re|burg@de
Mon Sep 7 16:22:31 CEST 2020
Dear Emanuele,
My answers see inline below.
Am 07.09.2020 um 15:38 schrieb Emanuele F. Osimo:
> Dear Gerta,
>
> 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?
Inverse variance weighting is relatively uncontroversial if data are
continuous and the fixed (more precise: the common) effect model is
used. (In parentheses: There have been a few controversies with respect
to the random effects model, but the majority of statisticians would
recommend it.) For binary data, the inverse variance method had also
been recommended for a long time, but most of us become more and more
aware that the two-stage methods are not optimal, particularly in case
of rare events.
If one study is much bigger than the others, the philosophy of the
common effect model says that the large study provides the most precise
estimate, and consequently dominates the result.
The philosophy of the random effects model says that each study somehow
has it own rights and therefore gives a little more weight to the
smaller studies. But if there are only few of them, the problem is that
the heterogeneity variance becomes difficult to estimate.
> 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?
As I am not very used to metafor (I mostly use meta), there are others
more qualified to answer this. If I understand this correctly, rma.glmm
uses a one-stage approach which avoids the problems with two-stage
approaches mentioned above.
I am not sure whether you make a distinction between OR and logOR? There
is no difference between the models with respect to this - they all
model the log of the OR, and the standard errors you have most probably
also refer to the log OR.
Best,
Gerta
>
> Many thanks again for your help.
>
> Best wishes,
>
> Emanuele
>
>
> On Fri, 4 Sep 2020 at 11:55, Gerta Ruecker
> <ruecker using imbi.uni-freiburg.de <mailto:ruecker using imbi.uni-freiburg.de>>
> wrote:
>
> 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.
>
> Best,
>
> Gerta
>
> 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
> unrelated
> > 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 years).
> > 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 to
> > adjust for time to follow-up? And how?
> >
> > Many thanks in advance for your time and thoughts on this.
> >
> > Best wishes,
> >
> > Emanuele
> >
> > [[alternative HTML version deleted]]
> >
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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
> <mailto:ruecker using imbi.uni-freiburg.de>
> Homepage:
> https://www.uniklinik-freiburg.de/imbi-en/employees.html?imbiuser=ruecker
>
--
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
Homepage: https://www.uniklinik-freiburg.de/imbi-en/employees.html?imbiuser=ruecker
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