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

table(dat$trt, dat$study)

do not have a variance per row, since each row is a single measurement. So,
you fitted some ANOVA model to these data (per trial) in order to obtain
the MSE and then want to use that as the variance for all yield values from
that trial.

variance you want. For example:

??
dat <- read.table(header=TRUE, text = "
trt     trial   bk    yield
Check   3       1     2493
Check   3       2     2173
Check   3       3     2628
Check   3       4     2168
Fox     3       1     3194
Fox     3       2     2363
Fox     3       3     2887
Fox     3       4     3278
NTX     3       1     2988
NTX     3       2     2361
NTX     3       3     2341
NTX     3       4     3218")

res <- lm(yield ~ trt + factor(bk), data=dat)
summary(res)
??
sigma(res)^2 ### error variance

### or treating 'bk' as random, but this yields the same results

library(nlme)
res <- lme(yield ~ trt, random = ~ 1 | bk, data=dat)
summary(res)
sigma(res)^2 ### error variance

Using aov() would require restructuring the data, but will also yield the
same results.

But if you are doing all of that anyway, why not just analyze ALL of the
data that way, adding trial as another random effect?

-----Original Message-----
From: Juan Pablo Edwards Molina [mailto:edwardsmolina at gmail.com]
Sent: Wednesday, August 09, 2017 02:41
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

Please do not consider my last reply!!
I mixed everything (because I am also estimating slopes and intercepts
with the same data set...)

I'm performing a network meta-analysis to estimate the treatments yield
difference with the untreated check.

this is my data structure,

?trt     trial   bk    yield
Check   3       1     2493
Check   3       2     2173
Check   3       3     2628
Check   3       4     2168
Fox     3       1     3194
Fox     3       2     2363
Fox     3       3     2887
Fox     3       4     3278
NTX     3       1     2988
NTX     3       2     2361
NTX     3       3     2341
NTX     3       4     3218

yield = plot grain yield at crop maturity (single value). Actually, plots
were ~ 15m?, however the grain weight was expressed in kg/10000 m? (1ha). ?
bk = are the blcoks within each trial (4 or 5).
trt = fungicide tratments to reduce a soybean disease.

I estimated yield difference (with the check) by setting Check as
reference level in the following model:

?net1 <- rma.mv(yield_mean, vi2, mods = ~ treatment, random = ~
treatment| trial,
                         method="ML", struct="UN", data=df)

?where yield_mean is the vector of treatments yield means and vi2 is the
vector of sampling variances obtained by:

vi2 <- V_yield/n  (for each trial)

(V_yield = MSE from anova)

?Since I have the raw full dataset,  I wonder if the correct would be to
include a block? random effect.

Sorry again...

Juan
? Edwards?

On Wed, Aug 9, 2017 at 6:34 AM, Viechtbauer Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
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