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

[James Pustejovsky]

To add to Wolfgang's suggestions, it's also possible to test for
differences between positive and negative symptoms using robust variance
estimation. The advantage of doing so is that the hypothesis
test/confidence interval is not predicated on having accurately imputed the
within-study correlations between effect size estimates. Example code below.

James

library(clubSandwich)

# Separate intercepts model
MultiMeta <- rma.mv(yi = yi, V = Vlist, mods = ~factor(posneg)-1, random =
~factor(posneg)|studyid, struct = "UN", data = mydata_multi)
Wald_test(MultiMeta, constraints = matrix(c(-1,1), 1), vcov = "CR2")

# With reference level
MultMeta <- rma.mv(yi = yi, V = Vlist, mods = ~factor(posneg), random =
~factor(posneg)|studyid, struct = "UN", data = mydata_multi)
coef_test(MultMeta, vcov = "CR2")
conf_int(MultMeta, vcov = "CR2")

On Tue, Oct 30, 2018 at 1:33 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> 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
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