[R-meta] IPD meta analysis / complex survey design

GOSLING Corentin corent|n@go@||ng @end|ng |rom gm@||@com
Fri Mar 5 10:32:51 CET 2021

Dear Prof Viechtbauer,

Thank you very much for your reply!

Sorry, my question was a bit misleading. In line with your suggestion, our
aim is to avoid merging ‘marginal ‘coefficients and ‘conditional’
coefficients by using only the svyglm function as soon as the data has a
complex structure (clustering and/or weighting, etc...).

You are entirely right, in situations with clustering only, we could
compare 3 approaches : (i) select only 1 individual per cluster and use glm
function, or keep clustering and use (ii) glmer function or (iii) svyglm
function. However, we are a bit reluctant to make these comparisons for two
reasons. First, as soon as data have a more complex structure (e.g.
sampling weights), the only approach allowing to take this into account is
the svyglm function. This makes comparisons a bit strange, as in our
examples, since one analysis is taking account of some specificity of the
design while the others are not. Second, from a practical point of
view, the burden on authors will become even more complicated as the time
required for analysis is already sometimes quite long (in particular
because of several multiple imputation models). We are concerned that the
multiplication of tests may sometimes make the analysis time so long that
it may discourage some authors from participating.

Our question was whether - within the same meta-analysis - we could "safely
*" *include effect sizes estimated by a standard logistic regression (when
data have a regular structure) +  effect sizes estimated by the svyglm
function (when the data have a complex structure). By safely, I mean without
having to compare the results of the svyglm function to other functions
(such as glm or glmer) when data have a complex structure.

If this is not possible, a more anecdotal question was whether it is
possible to "safely" include  effect sizes estimated by a  standard logistic
regression (when data have a regular structure) + effect sizes estimated by
the glmer function (when data have clustering).

Thank you so much for your help!

Best wishes

Corentin Gosling

Le ven. 5 mars 2021 à 09:32, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> a écrit :

> Dear Corentin,
> I cannot answer your question directly, that is, to what extent those
> results are comparable to each other, although if svyglm() gives 'marginal'
> (population averaged) coefficients in the sense of what a GEE model would
> do, then one could argue that those should not be combined with
> 'conditional' coefficients that glmer() provides (searching for
> combinations of terms like "GEE, marginal, population averaged, logistic
> mixed-effects, conditional, subject-specific" should turn up relevant
> discussions / papers).
> But leaving this aside, one could also just approach this issue entirely
> empirically, that is, simply code the type of analysis / type of
> coefficient for each study and examine in a moderator analysis whether
> there are systematic differences between the different types.
> Best,
> Wolfgang
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On
> >Behalf Of GOSLING Corentin
> >Sent: Thursday, 04 March, 2021 11:29
> >To: r-sig-meta-analysis using r-project.org
> >Subject: [R-meta] IPD meta analysis / complex survey design
> >
> >Dear all
> >
> >I come back to you about the IPD meta-analysis we are conducting to
> explore
> >the effect of month of birth on the persistence of ADHD. I had already
> >asked for your help a few months ago when I was writing the protocol. We
> >have since completed our systematic review and started to include data
> from
> >different cohorts. As the month of birth is sensitive data, we do not ask
> >the authors to send us the raw data: we have constructed an R-script that
> >we send to the authors and which performs the analyses automatically and
> >shares the anonymised results. We then carry out a classic two-stage
> >meta-analysis based on summary results.
> >
> >We are facing a new challenge that we did not anticipate. Several studies
> >involve complex survey design. Some studies have clusters (e.g., twin
> >cohorts or assessments of several regular siblings per family), while
> >others have even more complex sampling (and include for example sampling
> >weights, stratum or finite population correction (fpc)). Some studies
> >include both (clusters + stratum/weights/fpc).
> >
> >To analyse the data with clustering, naturally we thought of using mixed
> >models via the glmer function of lme4 (our VD is binary: ADHD persistence
> >yes/no). However, lme4 does not allow to handle - for the moment -
> sampling
> >weights or stratifications. Therefore, for all data with clustering and/or
> >weights and/or stratum and/or fpc, our idea was to use only the svyglm
> >function of the survey package in order to have a coherent group of
> >analyses (we know that the glmer and svyglm functions do not use the same
> >coefficients (marginals vs. conditionals)).
> >
> >Our question is the following: can we group within the same meta-analysis
> >coefficients that come from standard logistic regressions and coefficients
> >that come from generalised mixed models fitted using glmer or generalised
> >linear models adapted to complex designs fitted using svyglm?
> >
> >To support our question, we performed some tests on a dataset including
> >clusters and sampling weights. Here are the results :
> >
> >[...]
> >
> >As you can see, the results are almost the same from the models, except
> >when we take into account sampling weights. I hope that our problem is
> >clearly exposed
> >
> >Thank you very much in advance for your help!
> >
> >Corentin J Gosling

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