Dear Hanna,
You could perhaps try $SUBROUTINES ADVAN13 TOL=9 to check if this is related to
the accuracy of the solutions of the differential equations. ADVAN13 runs
faster than ADVAN6 and/or allows a higher tolerance setting.
Best regards,
Erik
________________________________
From: owner-nmus...@globomaxnm.com [owner-nmus...@globomaxnm.com] on behalf of
Silber Baumann, Hanna [hanna.silber_baum...@roche.com]
Sent: Monday, December 12, 2016 10:13 AM
To: nmusers@globomaxnm.com
Subject: [NMusers] Different results with ADVAN4 and ADVAN6
Dear nmusers,
I have a data set which contains single and multiple ascending dose data. The
model development was initially performed on the single dose data.
I initially developed a model using ADVAN4 TRANS 2 (2 compartment linear model
with oral administration) which I later reparameterized into ADVAN6. I expected
to see some minor differences in parameter estimates, OFV etc due to the change
in subroutine but was surprised to see large differences in both parameter
estimates and OFV (+180 points) but also a significant improvement in overall
fit (graphically) while the data was the same. With the ADVAN4 the model fit
was particularly poor to parts of the multiple dose data, with the ADVAN6 the
overall fit to all data was much improved. I was using NONMEM7.3 for the
analysis.
I guess the ADVAN4 model gets stuck in a local minima, but using the final
estimates from the ADVAN6 model does not help. I would be grateful for an
explanation of the reasons why this happens.
I have included the two models below.
Kind regards,
Hanna Silber
$PROBLEM PK with ADVAN4
$INPUT C ID TAD TIME AMT DV EVID CMT PTIM LDV DOSE BW BMI CLCR SEX AGE
STUDY DAY BLQ
$DATA nmpk05DEC16.csv IGNORE=@
$SUBROUTINES ADVAN4 TRANS4
$PK
CL = THETA(1) * EXP(ETA(1))
V2 = THETA(2) * EXP(ETA(2))
KA = THETA(3) * EXP(ETA(3))
ALAG1 = THETA(6) * EXP(ETA(4))
Q = THETA(7) * EXP(ETA(5))
V3 = THETA(8) * EXP(ETA(6))
S2 = V2/1000
$ERROR
IPRED = F
W = SQRT(THETA(4)**2*IPRED**2 + THETA(5)**2)
Y = IPRED + W*EPS(1)
IRES = DV-IPRED
IWRES = IRES/W
$THETA
(0,12.7) ;1 CL
(0,275) ;2 V2
(0,3.06) ;3 KA
(0, 0.12) ;4 Prop.RE (sd)
(0, 0.0153) ;5 Add.RE (sd)
(0,0.474) ;6 ALAG1
(0,26.3) ;7 Q
(0,133) ;8 V3
$OMEGA BLOCK(2) 0.0747 ;1 IIV CL
0.0723 0.0942 ;2 IIV V2
$OMEGA
1.76 ;3 IIV KA
0.00166 ;4 IIV ALAG
0.036 ;5 IIV Q
0.0407 ;6 IIV V3
$SIGMA
1 FIX ;
$EST METHOD=1 INTER MAXEVAL=9999 NOABORT SIG=3 PRINT=1 POSTHOC
$COV
######################################################
$PROBLEM PK with ADVAN6
$INPUT C ID TAD TIME AMT DV EVID CMT PTIM LDV DOSE BW BMI CLCR SEX AGE
STUDY DAY BLQ
$DATA nmpk05DEC16.csv IGNORE=@
$SUBROUTINES ADVAN6 TOL=5
$MODEL
COMP = (ABS) ;1
COMP = (CENT) ;2
COMP = (PER) ;3
$PK
CL = THETA(1) * EXP(ETA(1))
V2 = THETA(2) * EXP(ETA(2))
KA = THETA(3) * EXP(ETA(3))
ALAG1 = THETA(6) * EXP(ETA(4))
Q = THETA(7) * EXP(ETA(5))
V3 = THETA(8) * EXP(ETA(6))
K=CL/V2
K23 = Q/V2
K32 = Q/V3
A_0(1) = 0
A_0(2) = 0
A_0(3) = 0
$DES
DADT(1) = -KA*A(1)
DADT(2) = KA*A(1) - K*A(2) - K23*A(2) + K32*A(3)
DADT(3) = K23*A(2) - K23*A(3)
$ERROR
CONC = A(2)*1000/V2
IPRED = CONC
IF(CONC.EQ.0) IPRED = 1
W = SQRT(THETA(4)**2*IPRED**2 + THETA(5)**2)
Y = IPRED + W*EPS(1)
IRES = DV-IPRED
IWRES = IRES/W
$THETA
(0,12.1) ;1 CL
(0,275) ;2 V2
(0,3.06) ;3 KA
(0, 0.12) ;4 Prop.RE (sd)
(0, 0.0153) ;5 Add.RE (sd)
(0,0.474) ;6 ALAG1
(0,26.3) ;7 Q
(0,133) ;8 V3
$OMEGA BLOCK(2) 0.0747 ;1 IIV CL
0.0723 0.0942 ;2 IIV V2
$OMEGA
1.76 ;3 IIV KA
0.00166 ;4 IIV ALAG
0.036 ;5 IIV Q
0.0407 ;6 IIV V3
$SIGMA
1 FIX ;
$EST METHOD=1 INTER MAXEVAL=9999 NOABORT SIG=3 PRINT=1 POSTHOC
$COV
###############################
Data set example:
C ID TAD TIME AMT DV EVID CMT PTIM LDV
DOSE BW BMI CLCR SEX AGE STUDY DAY BLQ
0 11001 0 0 5 0 1 1 0 0
5 54.8 20.63 74.32657 0 44 1 1 0
0 11001 0.5 0.5 0 1.94 0 2 0.5
0.662688 5 54.8 20.63 74.32657 0 44 1
1 0
0 11001 1 1 0 14.6 0 2 1
2.681022 5 54.8 20.63 74.32657 0 44 1
1 0
0 11001 1.5 1.5 0 22.4 0 2 1.5
3.109061 5 54.8 20.63 74.32657 0 44 1
1 0
0 11001 2 2 0 18.1 0 2 2
2.895912 5 54.8 20.63 74.32657 0 44 1
1 0
0 11001 2.5 2.5 0 15.4 0 2 2.5
2.734368 5 54.8 20.63 74.32657 0 44 1
1 0
0 11001 3 3 0 16.3 0 2 3
2.791165 5 54.8 20.63 74.32657 0 44 1
1 0
0 11001 4 4 0 15.5 0 2 4 2.74084
5 54.8 20.63 74.32657 0 44 1 1 0
0 11001 6 6 0 11.9 0 2 6
2.476538 5 54.8 20.63 74.32657 0 44 1
1 0
0 11001 8 8 0 11.5 0 2 8
2.442347 5 54.8 20.63 74.32657 0 44 1
1 0
0 11001 12 12 0 7.71 0 2 12
2.042518 5 54.8 20.63 74.32657 0 44 1
1 0
0 11001 16.017 16.017 0 8.71 0 2 16
2.164472 5 54.8 20.63 74.32657 0 44 1
2 0
0 11001 24 24 0 5.55 0 2 24
1.713798 5 54.8 20.63 74.32657 0 44 1
2 0
0 11001 48 48 0 3.5 0 2 48
1.252763 5 54.8 20.63 74.32657 0 44 1
3 0
0 11001 72 72 0 1.86 0 2 72
0.620576 5 54.8 20.63 74.32657 0 44 1
4 0
0 11001 120.883 120.883 0 0.597 0 2 120
-0.51584 5 54.8 20.63 74.32657 0 44 1
6 0
0 11001 144.9 144.9 0 0.356 0 2 144
-1.03282 5 54.8 20.63 74.32657 0 44 1
7 0
0 11001 168.883 168.883 0 0.177 0 2 168
-1.73161 5 54.8 20.63 74.32657 0 44 1
8 0
--
