[R-meta] network meta-analysis - include block (within-study) level
Viechtbauer Wolfgang (SP)
wolfgang.viechtbauer at maastrichtuniversity.nl
Tue Aug 8 23:03:07 CEST 2017
Dear Juan,
Could you show a bit of the data (structure)? In particular, does each block contain two treatments, so that the structure looks something like this?
trial block treatment mean
--------------------------
1 1 1 ...
1 1 2 ...
1 2 1 ...
1 2 2 ...
2 1 1 ...
2 1 2 ...
2 2 1 ...
2 2 2 ...
2 3 1 ...
2 3 2 ...
...
Also, do you have the raw mean and variance (or SD) and sample size for each row of the dataset? It seems like you are first fitting some kind of ANOVA within each study, but that might actually complicate things.
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Juan Pablo Edwards Molina
Sent: Tuesday, August 08, 2017 22:09
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] network meta-analysis - include block (within-study) level
Dear list,
I have a dataset containing crop field randomized block design experiments
with observations at plot level (experimental unit), and I want to estimate
the treatments grain yield difference relative to a untreated check.
net1 <- rma.mv(yield, vi2, mods = ~ treatment, random = ~ treatment| trial,
method="ML", struct="UN", data=df)
where yield is the vector of mean treatments yield for vi2 is the vector of
sampling variances obtained by:
vi2 <- V_yield/n (for each trial)
(V_yield = MSE from anova)
Do I need to include the block in the model? or using the experiment
treatments means will obtain the same results? I suppose something like:
net2 <- rma.mv(yield, vi2, mods = ~ treatment, random = ~ treatment| block|
trial,
method="ML", struct="UN", data=df)
If the latter would be a better approach, how do I include the sampling
variance?
Thanks in advance,
Juan Edwards
More information about the R-sig-meta-analysis
mailing list