R help - Jul 2013 - Meta-analysis on a repeated measures design with multiple trials per subject using metafor

Hi all,

I am currently attempting to compile a summary of a series of five 
psychological experiments, and I am trying to do this using the metafor 
package. However, I am quite unsure which of the scenarios described in 
the metafor help pages applies to these data, because it is a repeated 
measures design, with multiple trials in each condition.

Assume that for every participant, I have a basic contingency table such 
as this one:

		treatment
		1   	2
response	
1		10 	20
2		20	10

(if this ASCII version does not work, I have 30 trials in each 
treatment, and participants give either response 1 or 2; the exact 
numbers don't matter)

The problem that I am trying to solve is how to convert these numbers to 
an effect size estimate that I can use with metafor.

As far as I understand it, I can only use it to get an effect size for 
outcomes that are dichotomous; i.e., either 1 or 0 for any subject. 
However, I have proportion data for every participant.

I have considered and tried these strategies:

1. Base the effect size on within-participant proportion differences. 
That is, in the table above, the treatment effect would be 
(20/30)-(10/30) = 1/3; and I would take the M and SD of these values to 
estimate a study-level effect ("MN" measure in metafor).

2. Use the overall treatment * response contingency table, ignoring the 
fact that these counts come from different participants ("PHI" or
"OR"
measures in metafor). In a study with 10 participants, I would get cell 
counts around 150.

However, from the research I've done into this topic, I know that 1) is 
not applicable to (as far as I understand) an odds ratio, and I suspect 
2) overestimates the effect.

A third method would be to use the regression coefficients, that I can 
easily obtain since I have all the raw data that I need. However, it is 
unclear to me whether and if yes, how I can use these in the metafor 
package.

 From my understanding of another message about this topic I found on 
this list (1), I understand that having access to the raw data is an 
advantage, but I am not sure whether the scenario mentioned applies to 
my situation.

1: 
http://r.789695.n4.nabble.com/meta-analysis-with-repeated-measure-designs-td2252644.html

I would very much appreciate any suggestions or hints on this topic.

Regards,
Marc

Dear Marc,

Let me see if I understand the type of data you have. You say that you have 5
experiments. And within each experiment, you have n subjects and for each
subject, you have data in the form described in your post. Now for each subject,
you want to calculate some kind of measure that quantifies how much more likely
it was that subjects gave/chose response 2 under treatment 2 versus treatment 1.
So, you would have n such values. And then you want to pool those values over
the n subjects within a particular experiment and then ultimately over the 5
experiments. Is that correct so far?

Assuming I got this right, let me ask you about those data that you have for
each subject. In particular, are these paired data? In other words, is there are
1:1 relationship between the 30 trials under treatment 1 versus treatment 2? Or
phrased yet another way, can you construct a table like this for every subject:

                trt 2
             ------------
             resp1 resp2                  
trt 1 resp1  a     b      10
      resp2  c     d      20
             20    10     30

Note that I added the marginal counts based on your example data, but this is
not sufficient to reconstruct how often response 1 was chosen for the same trial
under both treatment 1 and treatment 2 (cell "a"). And so on for the
other 3 cells.

If all of this applies, then essentially you are dealing with dependent
proportions and you can calculate the difference y = (20/30)-(10/30) as you have
done. The corresponding sampling variance can be estimated with v = var(y) =
(a+b)*(c+d)/t^3 + (a+c)*(b+d)/t^3 - 2*(a*d/t^3 - b*c/t^3) (where t is the number
of trials, i.e., 30 in the example above). See, for example, section 10.1.1. in
Agresti (2002) (Categorical data analysis, 2nd ed.).

So, ultimately, you will have n values of y and v for a particular experiment
and then the same thing for all 5 experiments. You can then pool those values
with rma(yi, vi) in metafor (yi and vi being the vectors of the y and v values).
You probably want to add a factor to the model that indicates which experiment
those values came from. So, something like: rma(yi, vi, mods = ~
factor(experiment)).

Well, I hope that I understood your data correctly.

Best,
Wolfgang

--
Wolfgang Viechtbauer, Ph.D., Statistician
Department of Psychiatry and Psychology
School for Mental Health and Neuroscience
Faculty of Health, Medicine, and Life Sciences
Maastricht University, P.O. Box 616 (VIJV1)
6200 MD Maastricht, The Netherlands
+31 (43) 388-4170 | http://www.wvbauer.com
________________________________________
From: r-help-bounces at r-project.org [r-help-bounces at r-project.org] On
Behalf Of Marc Heerdink [m.w.heerdink at uva.nl]
Sent: Wednesday, July 03, 2013 2:15 PM
To: r-help at r-project.org
Subject: [R] Meta-analysis on a repeated measures design with multiple trials
per subject using metafor

Hi all,

I am currently attempting to compile a summary of a series of five
psychological experiments, and I am trying to do this using the metafor
package. However, I am quite unsure which of the scenarios described in
the metafor help pages applies to these data, because it is a repeated
measures design, with multiple trials in each condition.

Assume that for every participant, I have a basic contingency table such
as this one:

                treatment
                1       2
response
1               10      20
2               20      10

(if this ASCII version does not work, I have 30 trials in each
treatment, and participants give either response 1 or 2; the exact
numbers don't matter)

The problem that I am trying to solve is how to convert these numbers to
an effect size estimate that I can use with metafor.

As far as I understand it, I can only use it to get an effect size for
outcomes that are dichotomous; i.e., either 1 or 0 for any subject.
However, I have proportion data for every participant.

I have considered and tried these strategies:

1. Base the effect size on within-participant proportion differences.
That is, in the table above, the treatment effect would be
(20/30)-(10/30) = 1/3; and I would take the M and SD of these values to
estimate a study-level effect ("MN" measure in metafor).

2. Use the overall treatment * response contingency table, ignoring the
fact that these counts come from different participants ("PHI" or
"OR"
measures in metafor). In a study with 10 participants, I would get cell
counts around 150.

However, from the research I've done into this topic, I know that 1) is
not applicable to (as far as I understand) an odds ratio, and I suspect
2) overestimates the effect.

A third method would be to use the regression coefficients, that I can
easily obtain since I have all the raw data that I need. However, it is
unclear to me whether and if yes, how I can use these in the metafor
package.

 From my understanding of another message about this topic I found on
this list (1), I understand that having access to the raw data is an
advantage, but I am not sure whether the scenario mentioned applies to
my situation.

1:
http://r.789695.n4.nabble.com/meta-analysis-with-repeated-measure-designs-td2252644.html

I would very much appreciate any suggestions or hints on this topic.

Regards,
Marc
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