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

[Viechtbauer, Wolfgang (SP)]

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