NONMEM 7.4.3 is the most up-to-date in usage of NPDE on BLQ records. Robert J. Bauer, Ph.D. Senior Director Pharmacometrics R&D ICON Early Phase 820 W. Diamond Avenue Suite 100 Gaithersburg, MD 20878 Office: (215) 616-6428 Mobile: (925) 286-0769 [email protected]<mailto:[email protected]> www.iconplc.com<http://www.iconplc.com/>
From: [email protected] [mailto:[email protected]] On Behalf Of STANDING, Joseph (GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS FOUNDATION TRUST) Sent: Monday, February 25, 2019 1:32 AM To: Smit, Cornelis (Klinische Farmacie); [email protected] Subject: RE: [NMusers] Strange PRED prediction in SAEM with M3 BQL handling Dear Cornelis, Please have a look at the following for how to visualise NPDEs with a "PRED" for BLQ data: Nguyen THT, Comets E. Mentre ́ F. Extension of NPDE for evaluation of nonlinear mixed effect models in presence of data below the quantification limit with applications to HIV dynamic model. J Pharmacokinet Pharmacodyn (2012) 39:499–518 This is possible to implement in NONMEM as per the 7.4 userguide NPDE section for the code. BW, Joe Joseph F Standing MRC Fellow, UCL Institute of Child Health Antimicrobial Pharmacist, Great Ormond Street Hospital Honorary Senior Lecturer, St George's University of London Tel: +44(0)207 905 2370 Mobile: +44(0)7970 572435 ________________________________________ From: [email protected] [[email protected]] on behalf of Smit, Cornelis (Klinische Farmacie) [[email protected]] Sent: 22 February 2019 10:11 To: [email protected] Subject: RE: [NMusers] Strange PRED prediction in SAEM with M3 BQL handling 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 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2691472/<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2691472/> ). 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: [email protected] [mailto:[email protected]] Namens Andrew Tse Verzonden: vrijdag 22 februari 2019 10:23 Aan: [email protected] 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: $PK TVCL=THETA(1) MU_1=LOG(TVCL) CL=EXP(MU_1+ETA(1)) TVV2=THETA(2) MU_2=LOG(TVV2) V2=EXP(MU_2+ETA(2)) TVQ=THETA(3) MU_3=LOG(TVQ) Q=EXP(MU_3+ETA(3)) TVV3=THETA(4) MU_4=LOG(TVV3) V3=EXP(MU_4+ETA(4)) K23=Q/V2 ;Distribution rate constant K32=Q/V3 ;Distribution rate constant KA=0 A_0(1)=0 A_0(2)=0 A_0(3)=0 $DES 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) $ERROR IPRED=A(2)/V2 W=SQRT(THETA(5)**2+((THETA(6)*IPRED)**2)) 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 IF(BQL.EQ.0) THEN F_FLAG=0 Y=IPRED+W*ERR(1) ELSE F_FLAG=1 ;BQL so Y is likelihood Y=PHI((LIM-IPRED)/W) ENDIF IWRES=(DV-IPRED)/W IRES=DV-IPRED 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. 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