[R-meta] Multivariate (multi-outcomes) meta-analysis

[Maciej Behnke]

Dear All,

I would like to run a meta-analysis for the effects of emotion on
physiological indexes reactivity (e.g., heart rate, blood pressure, skin
temperature, etc.). My initial idea was to run series of univariate
multilevel MA, for each pair of emotions and each physiological index (in
sum, 21 physio indexes). However, in the rejection note from the major
psychological journal,  the reviewers suggested that emotions are
multivariate phenomena – when you experience strong emotions you display
the physiological reactivity indexed by multiple (multivariate) indexes
which are correlated. Thus, reviewers suggested using the multivariate MA
approach to account for the relations between physiological indexes.
Although I believe it is a great suggestion, it is hard to implement to my
dataset. I found 3 main issues that raise my doubts about the superiority
of the multivariate MA approach.

1)      To implement the multivariate MA approach, I would need to know the
correlation matrix between the physio indexes. However, only 7 out of 110
papers reported some correlations; thus, I would need to estimate the
correlations based on data I collected in my lab during other experiments
(a new limitation for the study).

2)      I have tried to implement a multivariate MA approach using mixmeta
R package. Unfortunately, in my dataset, there are many situations in which
two or more physio indexes were never observed jointly. Thus, I inputted
missing values to base some estimations entirely on the indirect
comparison. With this approach, I was able to build the multivariate model
for 11 physio indexes.  However, adding more indexes caused that
convergence not reached after maximum number of iterations or there are
additional errors due to the missing values. Thus, I was not able to create
the model for all physio indexes.

3)      There are only minimal differences between the conclusions from the
univariate and multivariate models that I was able to pull from my dataset.

In sum, do you know any approach that I could implement to my data? Or
would you suggest addressing these issues as the limitations of the
univariate multilevel MA approach that I originally implemented?

Best,
Maciek

Maciej Behnke
Manager of Psychophysiology and Health Lab
Faculty of Psychology and Cognitive Science
Adam Mickiewicz University
89 Szamarzewskiego
<https://maps.google.com/?q=89+Szamarzewskiego&entry=gmail&source=g> Street
PL-60-568, Poznan, Poland
Tel. +48 725 859 990
E-mail: macbeh using amu.edu.pl

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