[R-meta] Subgroup correlated effects working model with additional random effects

[Andrea Asgeirsdottir]

Hello all,

I am conducting a meta-analysis on executive functions (EF) in adolescents. The meta-analysis includes studies that compare two groups of adolescents on at least one EF domain (inhibition, working memory, cognitive flexibility, decision-making, planning, verbal fluency). Not all studies included in the meta-analysis measure each domain. Most studies use several tasks to measure each domain. Often, more than one effect size is reported for each task. I am having some trouble specifying the working models, which I’ll then combine with RVE methods.

The first aim is to answer the question of whether one group is generally more impaired on executive functioning compared to the other group (independent of domains). For this I have the following: 

Variance-covariance matrix:
vEF_overall <- vcalc(
  vi = vi, 
  cluster = StudyID, 
  obs = ESID, 
  rho = 0.6, 
  data = adolEF
)

Overall-difference working model:
overallEF <- rma.mv(
  yi,
  V = vEF_overall,
  random = ~ ESID | StudyID,
  struct = "HCS",
  data = adolEF,
  method = "REML",
  sparse = TRUE
)

The second aim is to determine which subdomains show the most pronounced impairments. To compare EF domains, I have specified a variance-covariance matrix with DomainID as subgroup: 

Variance-covariance matrix for the subgroup correlated effects model:
vEF_SCE <- vcalc(
  vi = vi, 
  cluster = StudyID, 
  subgroup = DomainID
  obs = ESID, 
  rho = 0.6, 
  data = adolEF
)

Differences-between-domains working model (SCE model):
domainEF  <- rma.mv(
  yi ~ 0 + DomainID,
  V = vEF_SCE,
  random = list(~ DomainID | StudyID, ~ 1 | Task, ~ 1 | ESID),
  struct = "DIAG",
  data = adoleEF,
  method = "REML",
  sparse = TRUE
)

Do these specifications seem reasonable? I am unsure about the following:
1)  How to specify the random effects in the second working model. The tasks used to index each domain vary between studies, but each task is usually included in several studies. I followed Pustejovsky & Tipton (2021) for specifying a SCE model, but added random effects for task and effect sizes. 
2) Does it make sense to specify two separate variance-covariance matrices for the two working models? I’ve specified struct as “HCS” in the first one since not all studies assess all of the EF domains (after reading this: https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2023-July/004827.html) and as “DIAG”  in the subgroup (domains) model after seeing it specified like that in the example code provided by Pustejovsky & Tipton (https://osf.io/z27wt). Is it advisable to make these more specific, by e.g. including type = Task, since effect size estimates from tasks that tap the same EF domains can be expected to have correlated sampling errors?

Best wishes,
Andrea
---
Doctoral researcher
Omega lab, Department of Neurology
Max Planck Institute for Human Cognitive and Brain Sciences
Stephanstraße 1a
04103 Leipzig, Germany