[R-meta] Specifying V in nested subgroup analysis (rma.mv + clubSandwich)
|@w|@wt @end|ng |rom gm@||@com
Wed Dec 8 05:42:56 CET 2021
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.
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.
> 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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