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

Fri, Mar 5, 2021 12:32 AM

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 at r-project.org] On
Behalf Of GOSLING Corentin
Sent: Thursday, 04 March, 2021 11:29
To: r-sig-meta-analysis at 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

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

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