[R-meta] IPD meta analysis / complex survey design
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Fri Mar 5 12:07:39 CET 2021
Hi Corentin,
I did not mean to suggest that one should run several different analyses on a single dataset. That would indeed place too much of a burden on the authors of the individual studies.
My suggestion is really about this part:
>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).
I cannot tell you if is safe or not. But what you can always do is combine these different types in a single analysis and then check if there are systematic differences between these two types of effect sizes. If there are no systematic differences, then this is (empirical) evidence that combining them is in some sense an acceptable thing to do.
This approach is similar to checking if effect sizes extracted from published articles are systematically different from those extracted from unpublished sources in a meta-analysis. If there are systematic differences, we need to think about what the reason for the difference may be. If not, then this is one less thing to worry about.
Best,
Wolfgang
>-----Original Message-----
>From: GOSLING Corentin [mailto:corentin.gosling using gmail.com]
>Sent: Friday, 05 March, 2021 10:33
>To: Viechtbauer, Wolfgang (SP)
>Cc: r-sig-meta-analysis using r-project.org
>Subject: Re: [R-meta] IPD meta analysis / complex survey design
>
>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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