Paul
Your two models assume different inter-subject variability structures
that could explain the differences.
Try to run the same models with the full OMEGA matrix: then the problem
would become theoretically identical (although even in this case you can
see some differences that cannot be explained)
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
Paul Collier wrote:
Please could someone explain why I get very different results when using
ADVAN3 TRANS1 to model a data set compared with using ADVAN3 TRANS4?
Using the rate constants obtained with TRANS1 for a two compartment
model to compute clearance and volume terms gives different values to
those obtained with TRANS4. I have tried using various initial estimates
for the runs but this has not solved the problem. I have listed typical
outputs from Wings for NONMEM below for each of the two ways of
parameterising the two compartment model. The objective function is
considerably smaller with TRANS1 ( -580 compared with -533).
Thanks,
Paul
Paul S. Collier
School of Pharmacy
Queen's University Belfast
Email: [EMAIL PROTECTED]
*Using ADVAN3 TRANS1*
THETA: ELIMINATION RATE RATE VOLUME
ETA: OMEGA
ERR: SIGMA
run209.lst *-580.598* eval=209 sig=+3.4 sub=43
obs=184 CCIL=YNYN NVI1.1 PV1.0
THETA = 0.477 0.357 0.255 7.44
ETASD = 0.806846 0.000457165 0.00758947 1.40357
ERRSD = 0.242693
THETA:se% = 22.9 15.9 23.9 30.2
OMEGA:se% = 26.3 10287.1 559.0 27.7
SIGMA:se% = 23.4
MINIMIZATION SUCCESSFUL
P VAL.: 0.75E+00 0.17E+00 0.46E+00 0.88E+00
MIDAZOLAM 2-COMP RATES MODEL ADDITIVE ERROR
user 0:21.95 real 0:21.95 tcl 0:0.42
$PROBLEM MIDAZOLAM 2-COMP RATES MODEL EXPONENTIAL ERROR
$INPUT ID TIME DAT1=DROP AGE DOSE=AMT RATE DV WT BUC MDV GEN
$DATA ..\MIDAZOLAM.CSV
$SUBROUTINES ADVAN3 TRANS1
$PK
CALLFL=1
TVK=THETA(1)
K=TVK*EXP(ETA(1))
TVK12=THETA(2)
K12=TVK12*EXP(ETA(2))
TVK21=THETA(3)
K21=TVK21*EXP(ETA(3))
TVV=THETA(4)
V=TVV*EXP(ETA(4))
S1=V
$ERROR
IPRED=F
IRES=DV-IPRED
W=F
IWRES=IRES/W
Y=F*(1+ERR(1))
$THETA (0,0.5)
$THETA (0,0.5)
$THETA (0,0.5)
$THETA (0,10)
$OMEGA 0.01 0.01 0.01 0.01
$SIGMA 0.4
$ESTIMATION MAXEVAL=9999 PRINT=2 METHOD=CONDITIONAL INTERACTION
*Using ADVAN3 TRANS4*
THETA: CLEARANCE VOLUME1 INTERCOMPARTMENTAL CLEARANCE
VOLUME2
ETA: OMEGA
ERR: SIGMA
run213.lst *-533.475* eval=380 sig=+4.5 sub=43
obs=184 CCIL=YNYN NVI1.1 PV1.0
THETA = 3.19 0.209 9.87 12
ETASD = 1.25698 0.000467974 0.00130767 0.849706
ERRSD = 0.283019
THETA:se% = 20.4 85.2 49.2 31.2
OMEGA:se% = 15.5 20821.9 6432.7 43.6
SIGMA:se% = 24.5
MINIMIZATION SUCCESSFUL
P VAL.: 0.97E+00 0.93E+00 0.80E+00 0.31E+00
user 0:33.95 real 0:33.95 tcl 0:0.52
$PROBLEM MIDAZOLAM 2-COMP CLEARANCE MODEL
$INPUT ID TIME DAT1=DROP AGE DOSE=AMT RATE DV WT BUC MDV GEN
$DATA ..\MIDAZOLAM.CSV
$SUBROUTINES ADVAN3 TRANS4
$PK
CALLFL=-1
TVCL=THETA(1)
CL=TVCL*EXP(ETA(1))
TVV1=THETA(2)
V1=TVV1*EXP(ETA(2))
TVQ=THETA(3)
Q=TVQ*EXP(ETA(3))
TVV2=THETA(4)
V2=TVV2*EXP(ETA(4))
S1=V1
$ERROR
IPRED=F
IRES=DV-IPRED
W=F
IWRES=IRES/F
Y=F*(1+ERR(1))
$THETA (0,3)
$THETA (0,0.1)
$THETA (0,1)
$THETA (0,10)
$OMEGA 0.04 0.04 0.04 0.04
$SIGMA 0.4
$ESTIMATION MAXEVAL=9999 PRINT=2 METHOD=CONDITIONAL INTERACTION
$COVR PRINT=E
