[R-meta] Specifying V in nested subgroup analysis (rma.mv + clubSandwich)
James Pustejovsky
jepu@to @end|ng |rom gm@||@com
Wed Dec 8 05:03:29 CET 2021
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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