NONMEM Users Network Archive

Hosted by Cognigen

RE: [NMusers] Strange PRED prediction in SAEM with M3 BQL handling

From: Smit, Cornelis (Klinische Farmacie) <"Smit,>
Date: Fri, 22 Feb 2019 10:11:37 +0000

Hi Andrew,

When your observation is <BLQ, M3 gives you a likelihood of this value being < BLQ in the PRED column. So this value will be close to 1 when the model is fairly sure that the concentration should be BLQ. This might explain why the PREDs might be relatively high in your diagnostics here. I usually exlude the <BLQ values in my GOF diagnostics, and check for model misspecification with a VPC showing BLQ data (as described in ). You can do this with the old xpose package. I don’t think there is any way to visualize the BLQ prediction in the ‘usual’ GOF but I’m very curious if someone else has some ideas regarding this.

Kind regards,

Cornelis Smit
Hospital Pharmacist / PhD candidate

Dept. of Clinical Pharmacy
St. Antonius Hospital

Dept. of Pharmacology,
Leiden Academic Centre for Drug Research,
Leiden University, Leiden, The Netherlands

VRIJWARING: Dit e-mail bericht is uitsluitend bestemd voor de geadresseerde(n). Verstrekking aan en gebruik door anderen
is niet toegestaan. Als u niet de geadresseerde bent, stel dan de verzender hiervan op de hoogte en verwijder het bericht.
Aan de inhoud van dit bericht kunnen geen rechten worden ontleend.

Van: [] Namens Andrew Tse
Verzonden: vrijdag 22 februari 2019 10:23
Onderwerp: [NMusers] Strange PRED prediction in SAEM with M3 BQL handling

Dear all,

I am running SAEM with M3 BQL handling method via PsN but having some strange PRED values in mytab table if someone can shed some light:
I have tried using FOCE (excluding BQL data) & SAEM (excluding BQL data) both have normal looking fitting with data in individual plots.
Once I have coded SAEM with M3 codes and include BQL data it showed very strange PRED vs time plots (eg. 100 times over prediction at BQL time point). IPRED had normal results.

Here are the control stream that I have used:




 K23=Q/V2 ;Distribution rate constant
K32=Q/V3 ;Distribution rate constant


DADT(1)= -KA*A(1)
DADT(2)= -CL*A(2)/V2-K23*A(2)+K32*A(3)
DADT(3)= K23*A(2)-K32*A(3)



IF (LIMI.EQ.1) LIM= 0.05 ;BATCH 1
IF (LIMI.EQ.2) LIM= 0.01 ;BATCH 2
IF (LIMI.EQ.3) LIM= 0.025 ;BATCH 3

F_FLAG=1 ;BQL so Y is likelihood

My question is that whether there is error in my M3 $ERROR model? or whether PRED values for BQL means something else other than prediction for BQL data?

Thanks a lot.

Kind regards,
Andrew Tse

Research Pharmacist

Received on Fri Feb 22 2019 - 05:11:37 EST

The NONMEM Users Network is maintained by ICON plc. Requests to subscribe to the network should be sent to: Once subscribed, you may contribute to the discussion by emailing: