From: "Harnisch, Lutz, HMR/DE" <>
Subject: Modeling of inter study variability (ISV) as a third random effect level
Date: Mon, 31 May 1999 16:21:28 +0200

I don't know how to implement into NONMEM the model of a third random effect

level, like ISV.

Sampling individual ETA's from an additional OMEGA-element and allocating those for individual studies didn't work. I don't know how to maintain the individual ETA from one individual to the next within a single study. And furthermore how to get a new ETA for the first record of the next study.

Therefore I tried a different approach:
to sample all study ETA's at NEWIND.EQ.0 (i.e. at the first record in the dataset) with
$OMEGA BLOCK(1) 0.1 ; first sub study ETA(1)
$OMEGA BLOCK(1) SAME ; sec study ETA(2)
$OMEGA BLOCK(1) SAME ; last substudy ETA(n)

and allocated them as factors for instance on CL by

IS01 = EXP(ETA(1))
IS02 = EXP(ETA(2))
ISn = EXP(ETA(n))

FCL = 1
FCL = IS01
FCL = IS02

But the coding above causes all the gradients for the PK-model to be zero, even if I fix ETA(1) to zero, which means for my understanding that, all factors should become 1, and therefore, the gradients should be different from zero.

I really like to know if coding of ISV with NONMEM is possible, any help would be highly appreciated


Lutz Harnisch
Hoechst Marion Roussel | Product Realization | Biodynamics
Building H840 | Room 449 | D-65926-Frankfurt-Main | Germany
phone +49-69-305-16481 | fax +49-69-305-81990 |


Date: Mon, 31 May 1999 17:46:11 +0200
From: Pascal Girard <>
Subject: Re: Modeling of inter study variability (ISV) as a third random effect level

Dear Lutz,

Silvy Laporte has presented an implementation of such an ISV model at the last PAGE meeting in Wuppertal and a poster at ASCPT 98 in New Orleans. We have recently submitted a paper on this topic.

The way you implement it does not work since at NEWIND=0, NONMEM has no information to compute random study effect except for the study that appears on the very first record of your dataset.

We implemented ISV just like Mats implemented IOV. The trick is that in order to have a common ETA for every patient in one study, but different ETAs from one individual to another you have to set study number as the ID (ID=STUD) but no longer the patient identifier, create <<as many ETAs as the maximum number>> of patients in the biggest studies, then constraint all these ETAs to have the same variance.



Pascal Girard

Service Pharmacologie Clinique
BP 3041,162, avenue Lacassagne Tel : +33 (0)4 78 78 57 26
69394 LYON Cedex 03 FRANCE Fax : +33 (0)4 78 78 57 19


From: "Harnisch, Lutz, HMR/DE" <>
Subject: AW: Modeling of inter study variability (ISV) as a third random e ffec t level
Date: Mon, 31 May 1999 18:36:13 +0200

Dear Pascal,

Thank you very much for your ideas. We were puzzled about this topic already

at PAGE in Wuppertal.

In our pooled phase I analysis, the maximum number of individuals within a study was larger than 20 subjects/study, however, the number of studies were

below 20. Therefore, the introduction of more than 20 IOV corresponding to the maximum number of individuals might extent the limits in NONMEM, taking also into account that 20 IOVs for each parameter in the PK model are required, probably.

Therefore we decided to take only a single THETA for each study, and to summarize them in a second stage.

But what do you think about taking a small random sample of individuals from

each study (around 5 IOVs), and apply your approach?




From: "Piotrovskij, Vladimir [JanBe]" <>
Subject: RE: Modeling of inter study variability (ISV) as a third random effect level
Date: Tue, 1 Jun 1999 14:11:16 +0200


NONMEM is not the best choice for modelling multiple nested random effects.

The new S-PLUS library called nmle version 3.0 is more suitable. It is on beta-testing now and can be downloaded from the following sites:
Since it is already the 8th release, it is quite reliable, I believe.

Best regards,

Vladimir Piotrovsky, PhD
Clinical Pharmacokinetics, ext 5463
Janssen Research Foundation
2340 Beerse, Belgium