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 ao?t 2020 ? 20:34, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> a ?crit :
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 at r-project.org]
On Behalf Of GOSLING Corentin
Sent: Friday, 07 August, 2020 20:03
To: r-sig-meta-analysis at 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
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
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
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",
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,
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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