[R-meta] network meta-analysis - include block (within-study) level

[Viechtbauer Wolfgang (SP)]

So is 'y' is the mean treatment yield here? Also, is that really the average of multiple measurements (e.g., if there is subsampling)? Or is 'y' just the single measurement (yield) for that particular block and treatment? I still do not quite understand what kind of data you have. Also, what is 'x'?

Best,
Wolfgang

-----Original Message-----
From: Juan Pablo Edwards Molina [mailto:edwardsmolina at gmail.com] 
Sent: Tuesday, August 08, 2017 23:26
To: Viechtbauer Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] network meta-analysis - include block (within-study) level

Pretty close to that structure ​you say​:  I have ​several treatments at each block (balanced experiments), actually different set of treatments across the k-trials (all trials have the untreated Check)

This are a few lines of trial 3:
​
trt    trial bk  x    y
Check  3     1   40   2493
Check  3     2   45   2173
Check  3     3   40   2628
Check  3     4   40   2168
Fox    3     1   35   3194
Fox    3     2   30   2363
Fox    3     3   35   2887
Fox    3     4   30   3278
NTX    3     1   40   2988
NTX    3     2   35   2361
NTX    3     3   35   2341
NTX    3     4   35   3218
​
|​ 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.

Yes, I have the raw full dataset so I ​have the observation level ​values to calculate SD, means..​

Several authors from the Phytopathology area use ANOVA MSE :

"...The within-study variance (V) for IND or DON for these fungicide trials is the residual variance (mean square error) from an analysis of variance (ANOVA) of the effects of treatment on disease or toxin. Where the original data were available, this variance was calculated directly from an ANOVA..."

http://apsjournals.apsnet.org/doi/abs/10.1094/PHYTO-97-2-0211

Juan

On Tue, Aug 8, 2017 at 6:03 PM, Viechtbauer Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
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