[R] Meta-analysis on a repeated measures design with multiple trials per subject using metafor

Michael Dewey info at aghmed.fsnet.co.uk
Sat Jul 6 14:56:10 CEST 2013

```At 11:15 05/07/2013, Marc Heerdink wrote:
>Dear Wolfgang and other readers of the r-help list,
>
>Thank you very much for your suggestion. Unfortunately, the data
>that I have can not be described with a table such as the one you
>have made, because there's no identical trial under both treatment 1
>and treatment 2. To explain, let me explain a bit more about the experiments:
>
>* All subjects were presented with the same number of trials
>* Half of these trials were preceded by a prime from category 1
>(treatment 1) and half of these trials with a prime from category 2
>(treatment 2)
>* Subjects were asked to respond to these trials (a unique stimulus
>for each trial) by pressing one of two keys on the keyboard.
>
>Because everything was randomized, I can only calculate the total
>number of times a certain response was used under each type of
>trial. There is no pairing of trials under two treatments, so I am
>forced to use the marginal totals from your table.

But presumably you could calculate some statistic suitable for
summarising the relevant features here? Difference in proportions,
odds ratio, ...

>I have uploaded a simplified version of the data for one experiment
>to illustrate this (the actual experiments have five treatments and
>some have moderators):
>https://www.dropbox.com/s/rhgo12cm1asl6x8/exampledata.csv
>
>This is the script that I used to generate the data:
>https://www.dropbox.com/s/7uyeaexhnqiiy55/exampledata.R
>
>The problem thus appears to lie mainly in estimating the variance of
>the proportion difference from only the marginal totals, is that
>correct? Is there a way to calculate it from only the marginal totals?
>
>One alternative that I have tried over the last few days, is to use
>the b parameter of interest and it's corresponding standard error
>from the lme4 regression output that I use to analyse the individual
>experiments. Then, I use rma(yi, sei) to do a meta-analysis on these
>parameters. I am not sure this is correct though, since it takes
>into account between-subjects variance (through a random effect for
>subject), and it is sensitive to the covariates/moderators I include
>in the models that I get the b parameters from.

So you end up with 5 values of b? The fact that they adjust for
different moderators does not seem an issue to me, indeed it could be
argued to be an advantage of the meta-analytic approach here.

>Thanks again for your help, and for any suggestions for solving this problem!

I think we are all assuming you have different participants in each
experiment but I thought I would raise that as a question.

>Regards,
>Marc
>
>
>On 07/04/2013 11:21 PM, Viechtbauer Wolfgang (STAT) wrote:
>>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.
>>
>>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
>>______________________________________________
>>R-help at r-project.org mailing list
>>https://stat.ethz.ch/mailman/listinfo/r-help
>>and provide commented, minimal, self-contained, reproducible code.
>
>--
>Marc Heerdink, MSc. (PhD. candidate)
>Dept. of Social Psychology
>University of Amsterdam
>http://home.medewerker.uva.nl/m.w.heerdink/
>http://www.easi-lab.nl/
>
>

Michael Dewey
info at aghmed.fsnet.co.uk
http://www.aghmed.fsnet.co.uk/home.html

```