Implementing Nested Random Effects (Interstudy variability and Between subject variability)

Estimation
1

Dear Pumas Support Team,

I am conducting an individual patient data meta-analysis (IPDMA) pooling PK data from >10 clinical studies. I am intending to partition variability into:

  1. Between-subject variability (BSV) - variability between individuals within the same study

  2. Between-study variability (ISV) - variability between studies

In NONMEM, this was reported to be implemented using the $LEVEL record with nested random effects:

$LEVEL
STUDYID = (3 [1], 4 [2])

$PK
TVCL = THETA(1)
CL = TVCL * EXP(ETA(1) + ETA(3))  ; ETA(1)=BSV, ETA(3)=ISV nested within BSV

$OMEGA
0.09  ; BSV CL
0.09  ; BSV V
0.09  ; ISV CL (shared by all subjects in same study)
0.09  ; ISV V (shared by all subjects in same study)

The key feature is that subjects within the same study share the same ISV random effect value (e.g., all subjects with STUDYID=1 share the same ETA(3)), while each subject has its own unique BSV random effect. Pharmacokinetic analysis across studies to drive knowledge‐integration: A tutorial on individual patient data meta‐analysis (IPDMA) - PMC

My questions:

  1. Does Pumas currently support nested/hierarchical random effects where random effects can be indexed by a grouping variable (like STUDYID) rather than just by subject ID?

  2. Here are two things I have thought of, but I am not sure this would achieve the intended purpose, or be able to tease apart the BSV from ISV:
    CL = tvcl * exp(ηCLbsv + ηCLisv)
    CL = tvcl * exp(ηCLbsv + ηCLisv[STUDYID])

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