[R-meta] Comparing effect sizes in multivariate meta analysis

[Viechtbauer, Wolfgang (SP)]

Hi Rob,

You probably want to use struct="UN" here:

MultiMeta <- rma.mv(yi = yi, V = Vlist, mods = ~factor(posneg)-1, random = ~factor(posneg)|studyid, struct = "UN", data = mydata_multi)

Using struct="CS" assumes that the amount of heterogeneity is the same for positive and negative symptoms, which may not be the case.

To test for a difference between positive and negative symptoms, you can compute the contrast between the two estimates with:

anova(MultiMeta, L=c(-1,1))

Alternatively, fit the model with 'mods = ~factor(posneg)'. Then one of the two levels becomes the reference level (and hence the intercept) and the coefficient for the other level is the *difference* between the two levels. You should get the same p-value for this coefficient as when computing the contrast as shown above.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of McCutcheon, Robert
Sent: Tuesday, 30 October, 2018 17:14
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] Comparing effect sizes in multivariate meta analysis

Dear All,

I have data from a number of randomised placebo controlled trials, each trial reports data on the effect  of the drug on both ‘positive’ and ‘negative’ symptoms. I wish to determine whether the drug has a greater effect on negative or positive symptoms

As I do not know the within-study correlations I have estimated a covariance matrix using the ClubSandwich package, and intend to run the analysis with a range of values for ‘r’:

Vlist <- impute_covariance_matrix(vi = multistudy[[6]]$vi, cluster = mydata_multi$studyid, r=0.5 )

I then conduct the multivariate analysis as follows:

MultiMeta <- rma.mv(yi = yi, V = Vlist, mods = ~factor(posneg)-1, random = ~factor(posneg)|studyid, struct = "CS", data = mydata_multi)

Where yi is the calculated effect size, ‘posneg’ is the label describing whether the results of that row refers to positive or negative symptoms, and ‘studyid’  is a separate label for each study.

This gives me separate effect sizes for positive and negative symptoms but I wonder if anyone could advise how I test whether these effect sizes are significantly different form one another, i.e. whether the drug has a greater effect on positive as compared to negative symptoms?

Many thanks for your time,

Rob