[R-meta] Need to specify meta analysis weights
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Mon Sep 7 20:33:14 CEST 2020
Just chiming in briefly here: The answer to 2) is No. If you only have the log odds ratios and corresponding standard errors, then you cannot use rma.glmm(). To fit logistic regression models, you need the 2x2 table counts.
Best,
Wolfgang
>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org]
>On Behalf Of Gerta Ruecker
>Sent: Monday, 07 September, 2020 16:23
>To: Emanuele F. Osimo
>Cc: r-sig-meta-analysis using r-project.org
>Subject: Re: [R-meta] Need to specify meta analysis weights
>
>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
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