[R-meta] Specifying V in nested subgroup analysis (rma.mv + clubSandwich)

[Timothy MacKenzie]

Dear James,

Thanks for your reply. I wanted my single model (gg) to be equivalent
to gg1 and gg2 (below). But if you use: print(gg, digits = 7);
print(gg1, digits = 7) ;  print(gg2, digits = 7), you realize the
variance components don't quite match.

Now, if you change the first random term in gg1 and gg2 from: ~1 |
study to: ~study_type | study, we get a better match.

Also, James, neither reporting levels nor study_type levels co-occur
in any study.

Thanks,
Tim M

gg1=rma.mv(yi ~ 0+reporting, V, random = list(~1 | study, ~ reporting | obs),
           struct = c("DIAG","DIAG"),
           subset = study_type=="alternative", data = dat1)

gg2=rma.mv(yi ~ 0+reporting, V, random = list(~1 | study, ~ reporting | obs),
            struct = c("DIAG","DIAG"),
            subset = include=="yes" & study_type=="standard", data = dat1)

On Tue, Dec 7, 2021 at 10:03 PM James Pustejovsky <jepusto using gmail.com> wrote:
>
> Hi Tim,
>
> If the predictor study_type is a study-level variable, then setting
> subgroup = study_type will have no effect and should produce results
> identical to using subgroup = NULL.
>
> If you want to estimate average effect sizes for each reporting
> category based only on the direct evidence (estimates from composite
> scales contribute to the average for composite scales, estimates from
> subscales contribute to the average for subscales), then set subgroup
> = reporting.
>
> On the other hand, if you set subgroup = NULL, then the average
> effects for composite scales will be influenced a little bit by the
> effect size estimates from subscales that co-occur in the same study
> with estimates from composite scales, and the average effects for
> subscales will be influenced a little bit by the effect size estimates
> from composite scales that co-occur in the same study with estimates
> from subscales.
>
>
> James
>
> On Thu, Dec 2, 2021 at 2:29 PM Timothy MacKenzie <fswfswt using gmail.com> wrote:
> >
> > Dear Meta SIG Members,
> >
> > I'm running a nested subgroup analysis. That is:
> >
> > 1. Studies are subgrouped by "study_type" into standard vs. alternative.
> > 2. Each previously made subgroup is further subgrouped by "reporting"
> > into subscale vs. composite (see data example below).
> >
> > Effect sizes in each study are correlated (due to the individual study
> > designs) but my question is: given the "nested subgroup nature" of my
> > model, how should I specify the V (subgroup=NULL, or
> > subgroup=study_type, or subgroup=reporting)?
> >
> > Thanks,
> > Tim M
> >
> > (V <- with(dat1, impute_covariance_matrix(vi, study, r=.6,subgroup=NULL)))
> >
> > g<-rma.mv(yi ~ 0 + study_type:reporting, V, random = list(~study_type
> > | study, ~interaction(study_type,reporting) | obs), struct =
> > c("DIAG","DIAG"), data = dat1)
> >
> > m="
> > study subscale  reporting  obs include yi   vi         study_type
> > 1        A      subscale   1   yes     1.94 0.33503768 standard
> > 1        A      subscale   2   yes     1.06 0.01076604 standard
> > 2        A      subscale   3   yes     2.41 0.23767389 standard
> > 2        A      subscale   4   yes     2.34 0.37539841 standard
> > 3        A&C    composite  5   yes     3.09 0.31349510 standard
> > 3        A&C    composite  6   yes     3.99 0.01349510 standard
> > 4        A&B    composite  7   yes     2.90 0.91349510 standard
> > 4        A&B    composite  8   yes     3.01 0.99349510 standard
> > 5        G&H    composite  9   yes     1.01 0.99910197 alternative
> > 5        G&H    composite  10  yes     2.10 0.97910095 alternative
> > 6        E&G    composite  11  yes     0.11 0.27912095 alternative
> > 6        E&G    composite  12  yes     3.12 0.87910095 alternative
> > 7        E      subscale   13  yes     0.08 0.21670360 alternative
> > 7        G      subscale   14  yes     1.00 0.91597190 alternative
> > 8        F      subscale   15  yes     1.08 0.81670360 alternative
> > 8        E      subscale   16  yes     0.99 0.91297170 alternative"
> > dat1 <- read.table(text=m,h=T)
> >
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