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Re: Problem with handling BQL with M3 - additive & proportional error

From: Andreas Lindauer <alindauer-research>
Date: Thu, 8 Sep 2011 16:26:22 +0200

Hi Ann,

For me it seem that your model is clearly right ;-) Remember that the M3·
method estimates the likelihood of a concentration being below the LLOQ
when F_Flag=1 and reports it in the PRED column (the likelihood of BQL
data increases with increasing time after the last dose). IPRED, however,
is always reported as a concentration (in a PK model).

Thanks your referencing my post from last year. The original reference,
however, where I got the code from (which I customized slightly) is:
Jae Eun Ahn et al., “Likelihood based approaches to handling data below
the quantification limit using NONMEM VI,” Journal of Pharmacokinetics and
Pharmacodynamics 35, no. 4 (August 2008): 401-421.

Just that the right people get their merits...

Best regards, Andreas.

Dr. Andreas Lindauer
Modeling & Simulation and in vivo ADME
Dept. of Pharmacokinetics and Metabolism
R&D Center. Ferrer Internacional S.A.
Juan de Sada 32, 08028 Barcelona
Tel +34 93 509 3265
Fax +34 93 411 2764
alindauer-research
www.ferrergrupo.com







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Ann Rigby-Jones <ann.rigby-jones
Enviado por: owner-nmusers
08/09/2011 15:43

Para
"nmusers bomaxnm.com" <nmusers
cc
"alindauer-research
Asunto
[NMusers] Problem with handling BQL with M3 - additive & proportional
error






Dear NONMEM Users
 
I'm working (with NM7) on a data set in which 24% of the data is BLQ. The
data set comprises 39 individuals, all of whom received a single bolus
dose of an IV drug, with samples collected for 12 hours afterwards. To
deal with the BLQ samples, I'm trying to implement the M3 method using a
version with a combined additive and proportional model. I took the code
for this from Andreas Lindauer's post to NMUsers on 20th April 2010 (thank
you Andreas :-).
 
My models are minimising successfully and the $COV step is running,
however, something is clearly wrong :-S Here is the output for the first
subject from a 3 comp, naive pooled (no ETAs) model:
 
ID EVID AMT TIME IPRED CWRES DV PRED
RES WRES
1 1 6840000 0 0 0
    0 0 0 0
1 0 0 1 719.28 0 897.7
719.28 0 0
1 0 0 2 598.54 0 699.5
598.54 0 0
1 0 0 3 508.11 0 569 508.11
           0 0
1 0 0 5 387.57 0 425.1
387.57 0 0
1 0 0 10 252.56 0 279 252.56
         0 0
1 0 0 15 197.01 0 216.7
197.01 0 0
1 0 0 30 119.59 0 131.7
119.59 0 0
1 0 0 45 81.439 0 79.05
81.439 0 0
1 0 0 60 59.581 0 64.43
59.581 0 0
1 0 0 90 36.048 0 33.71
36.048 0 0
1 0 0 120 23.365 0 21.41 23.365
           0 0
1 0 0 150 15.457 0 15.39 15.457
           0 0
1 0 0 180 10.285 0 9.557 10.285
           0 0
1 0 0 240 4.5695 0 6.273 4.5695
           0 0
1 0 0 300 2.0317 0 0
0.60983 0 0
1 0 0 360 0.90337 0 0
0.65014 0 0
1 0 0 480 0.1786 0 0
0.67515 0 0
1 0 0 600 0.0353 0 0
0.67999 0 0
1 0 0 720 0.0069 0 0
0.68095 0 0
 
Firstly, I'm unclear as to why IPRED and PRED differ (for the BLQ samples,
300 minutes onwards) in the absence of ETAs. Secondly, you can see how
the IPRED predictions for the BQL points decrease over time as would be
expected, while the PRED concentrations rise? A model including ETA
parameters shows the same result i.e. IPRED predictions decrease over
time, PRED for BLQ samples rise.
 
My control stream is pasted below, grateful as always for any ideas :-)
 
With thanks and best wishes
 
Ann
_______________________________________________________________________
Ann Rigby-Jones PhD MRSC
Research Fellow in Pharmacokinetics & Pharmacodynamics
Peninsula College of Medicine & Dentistry
 
N31, ITTC Phase 1
Tamar Science Park
1 Davy Road
Derriford
Plymouth
PL6 8BX
_______________________________________________________________________
 
$PROB PATIENTS AND NORMAL CONTROLS
$INPUT ID TIME DV FLG BRN EVID AMT RATE SEX AGE HGT WGT
$DATA alldata.CSV IGNORE=#
$SUBROUTINES ADVAN11 TRANS4
 
$PK
TVCL=THETA(1) ;*WGT**0.75
TVQ2=THETA(2) ;*WGT**0.75
TVQ3=THETA(3) ;*WGT**0.75
TVV1=THETA(4) ;*WGT**1
TVV2=THETA(5) ;*WGT**1
TVV3=THETA(6) ;*WGT**1
 
 
SDADD = THETA(7) ; the standard deviation of the additive part
CVPROP = THETA(8) ; the CV of the proportional part
 
CL=TVCL*EXP(ETA(1))
Q2=TVQ2*EXP(ETA(2))
Q3=TVQ3*EXP(ETA(3))
V1=TVV1*EXP(ETA(4))
V2=TVV2*EXP(ETA(5))
V3=TVV3*EXP(ETA(6))
 
S1=V1
 
 
$THETA
(0, 482) ; CL
(0, 644) ; Q2
(0, 87) ; Q3
(0, 9747) ; V1
(0, 9356) ; V2
(0, 20300) ; V3
(0, 3) ;SDADD
(0, 0.2) ;CVPROP
 
 
$OMEGA
(0 FIX) ;CL
(0 FIX) ;Q2
(0 FIX) ;Q3
(0 FIX) ;V1
(0 FIX) ;V2
(0 FIX) ;V3
 
 
$ERROR
;M3-Method
LOQ=5
 
IPRED=F
IRES = DV-IPRED
W=SQRT(SDADD**2+CVPROP**2*IPRED**2)
DEL=0
IF(W.EQ.0) DEL=1
IWRES=IRES/(W+DEL)
 
DUM=(LOQ-IPRED)/(W+DEL)
CUMD=PHI(DUM)
 
IF (FLG.EQ.0) THEN
F_FLAG=0
Y = IPRED+ERR(1)*W
ELSE
F_FLAG=1
Y=CUMD
ENDIF
 
$SIGMA 1 FIX
 
$EST SIG=4 METHOD=1 INTER LAPLACIAN NUMERICAL SLOW NOABORT MAXEVAL=99999
PRINT=5
$COV
$TABLE ID EVID AMT TIME IPRED CWRES
       NOPRINT FILE=AllRecords.txt
$TABLE ID CL Q2 Q3 V1 V2 V3
       ETA1 ETA2 ETA3 ETA4 ETA5 ETA6
       FIRSTONLY NOPRINT NOAPPEND FILE=FirstRecords.txt

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Received on Thu Sep 08 2011 - 10:26:22 EDT

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