[R-meta] Moderation analysis in IPD meta-analysis

[Dr. Gerta Rücker]

Dear Corentin,

you may also be interested in the DATAShield project, see their website 
https://www.datashield.ac.uk/

Best,

Gerta

Am 08.08.2020 um 12:21 schrieb Michael Dewey:
> Dear Corentin
>
> I have not investigated this in detail but there are two packages on 
> CRAN you might want to look at.
>
> https://cran.r-project.org/package=multinma
>
> claims to be able to integrate IPD and aggregate data in one analysis
>
> https://cran.r-project.org/package=metagam
>
> which claims to be able to get the other researchers to run the 
> analysis and share it with you when they are not allowed to share the 
> data.
>
> As I say I have not looked at any of them in detail so this may be 
> wide of the mark but worth a brief look.
>
> Michael
>
> On 08/08/2020 09:14, GOSLING Corentin wrote:
>> Dear Pr Viechtbauer,
>>
>> Thank you very much for your answer!
>>
>> 1) Sorry for the family argument, I suppressed it when I copy/paste the
>> code.
>> 2) I was not aware of this solution in the glm function. I have 
>> compared it
>> with the initial solution using emmeans and it gives similar results. 
>> Since
>> your solution is definitively more elegant, we are going to use it.
>> 3) Great, it is very reassuring to have your confirmation! We had the
>> feeling that this was feasible but we were afraid to miss the reason
>> preventing us from applying our approach to a patient-level moderator.
>>
>> Thank you so much for your help!
>> Best
>> Corentin J Gosling
>>
>>
>> Le ven. 7 août 2020 à 20:34, Viechtbauer, Wolfgang (SP) <
>> wolfgang.viechtbauer using maastrichtuniversity.nl> a écrit :
>>
>>> Dear Corentin,
>>>
>>> Overall, your approach seems sound. But a few notes:
>>>
>>> 1) study1<-glm(DV~IV, data=datastudy1) is not logistic regression. You
>>> need:
>>>
>>> study1 <- glm(DV ~ IV, data=datastudy1, family=binomial)
>>>
>>> 2) I've only played around with emmeans a little bit, so can't 
>>> comment on
>>> that part. But I don't think you even need it. You can just fit the 
>>> model
>>> in such a way that you directly get the three log odds ratios for 
>>> the three
>>> levels of IV. In fact, the estimates of the three log odds ratios are
>>> independent, so one could even just fit three simple logistic 
>>> regression
>>> models that will give you the same results. An example:
>>>
>>> dat <- data.frame(DV = c(1,0,0,1,0,1,1,1,0,1,1,1),
>>>                    IV = c(1,3,2,3,5,3,7,7,4,9,6,3),
>>>                    VM = rep(c("a","b","c"),each=4))
>>>
>>> # parameterize logistic regression model so we get the three log odds
>>> ratios directly
>>> res <- glm(DV ~ VM + IV:VM - 1, data=dat, family=binomial)
>>> summary(res)
>>>
>>> # the covariance between the three estimates is 0
>>> round(vcov(res), 5)
>>>
>>> # show that the simple logistic regression model for a subset gives the
>>> same results
>>> res.a <- glm(DV ~ IV, data=dat, family=binomial, subset=VM=="a")
>>> summary(res.a)
>>>
>>> So actually the V matrix corresponding to the three log odds ratios is
>>> diagonal. But you still would want to account for potential 
>>> dependency in
>>> the underlying true log odds ratios, so the model
>>>
>>> model <- rma.mv(yi, V, mods = ~ VM-1, random=~VM|study, struct="UN",
>>> data=dat)
>>>
>>> is still appropriate (with V being diagonal, so you can also just 
>>> pass a
>>> vector with the sampling variances to rma.mv).
>>>
>>> 3) The statement that 2-stage approaches cannot be used to analyze
>>> patient-level moderators isn't quite true. If one actually analyzes the
>>> patient-level moderator in stage 1 (as you describe), then the 2-stage
>>> approach definitely allows you to examine such a patient-level 
>>> moderator.
>>>
>>> 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: Friday, 07 August, 2020 20:03
>>>> To: r-sig-meta-analysis using r-project.org
>>>> Subject: [R-meta] Moderation analysis in IPD meta-analysis
>>>>
>>>> Dear Metafor’s users,
>>>>
>>>> This is the first time that I post on this mailing list.
>>>>
>>>> Our team is currently planning an individual patient data 
>>>> meta-analysis of
>>>> prospective cohorts.
>>>>
>>>> We adopt an IPD approach because no prospective study has yet reported
>>> this
>>>> association while many should have the data to assess it (i.e., 
>>>> they have
>>>> access to the targeted variables but did not report the association).
>>>> Unfortunately, we anticipate that most of the included studies will 
>>>> not
>>>> have an ethics committee that gives the right to share their data 
>>>> with us.
>>>>
>>>> To overcome this, we plan to ask the authors of the included 
>>>> studies to
>>>> perform the analyses on their own data and to share only the 
>>>> results of
>>> the
>>>> analyses with us. Our dependent variable is binary (DV: yes/no) and 
>>>> our
>>>> independent variable is an ordered variable (IV: a scale variable 
>>>> in 12
>>>> points [from 1 to 12]) treated as a continuous variable.
>>>>
>>>> We ask authors to perform a logistic regression. Based on their 
>>>> results
>>>> (log odds ratio and associated variance), we adopt a classic two-stage
>>>> approach. I show the R code for a particular study to highlight our
>>>> approach.
>>>>
>>>> #R code for study 1
>>>>
>>>> study1<-glm(DV~IV, data=datastudy1)
>>>>
>>>> yi1<- summary(study1)$coefficients[2,1] #extract the log odds ratio
>>>>
>>>> vi1<- summary(study1)$coefficients[2,2]^2 #extract the squared 
>>>> standard
>>>> error
>>>>
>>>> # then, we repeat the same process for each included study
>>>>
>>>> # Once all the effect sizes and their variance are collected, we 
>>>> can store
>>>> them within a dataset and run a standard two stage meta-analysis
>>>>
>>>> dat<-data.frame(
>>>> yi=c(yi1, yi2, yi3…),
>>>> vi=c(vi1, vi2, vi3…),
>>>> study=c(1, 2, 3…))
>>>>
>>>> model<-rma(yi,vi, dat)
>>>>
>>>> This is the code for our primary analysis. In an exploratory 
>>>> analysis, we
>>>> would like to perform a moderation analysis with a patient-level
>>> moderator.
>>>> We understand how to perform a moderation analysis for a study-level
>>>> moderator but we are not sure on how to implement it with a 
>>>> patient-level
>>>> moderator. The aim of this moderation analysis will be to obtain the
>>>> estimated average effects for each level of a moderator. I describe 
>>>> here
>>>> the approach we have envisaged:
>>>>
>>>> # example of R code for study 1
>>>>
>>>> #let VM denote a participant-level moderator with 3 categories (a,b,c)
>>>>
>>>> study1<-glm(DV~IV*VM, data=datastudy1)
>>>>
>>>> EM1<-emmeans::emtrends(study1, ~VM, var=" IV")
>>>>
>>>> yi1<- as.data.frame(EM1$emtrends)[,2] #extract log odds ratio for each
>>>> level of the VM for study 1 (contains 3 values)
>>>>
>>>> V1<-vcov(EM1) # extract the variance/covariance matrix for study 1 
>>>> (a 3x3
>>>> matrix)
>>>>
>>>> # then, we can build a dataset which will look like this...
>>>>
>>>> dat<-data.frame(
>>>> yi=c(yi1, yi2, yi3…),
>>>> VM=c(a,b,c,a,b,c,a,b,c…),
>>>> study=c(1,1,1, 2,2,2, 3,3,3…))
>>>>
>>>> # ...and a variance-covariance matrix using the bldiag function
>>>>
>>>> V<-bldiag(list(V1,V2,V3…))
>>>>
>>>> # Last, we plan to perform a multivariate model in which we leave 
>>>> out the
>>>> model intercept and in which we use an unstructured variance 
>>>> structure (if
>>>> the model converges).
>>>>
>>>> model<-rma.mv(yi, V, mods = ~ VM-1, random=~VM|study, struct="UN",
>>> data=dat)
>>>>
>>>> We were wondering if you could give us some feedback on the 
>>>> correctness of
>>>> our approach. We have read in several textbooks that two-stage
>>>> meta-analyses are not designed to assess patient-level moderator but,
>>> given
>>>> that asking for raw data would probably decrease the likelihood of 
>>>> getting
>>>> return from authors of primary studies, we would prefer staying at a
>>>> two-stage approach.
>>>>
>>>> Thank you very much for your help and for this amazing mailing list!
>>>>
>>>> Corentin J Gosling
>>>> Charlotte Pinabiaux
>>>> Serge Caparos
>>>> Richard Delorme
>>>> Samuele Cortese
>>>
>>
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>>
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