Dear NMusers:
I am trying to fit a data set with concentrations of encapsulated drug and
released drug. These concentrations were measured after an hour infusion of
encapsulated drug. The problem is that I cannot get standard error estimated
from simultaneous fitting of all the data. And I got an error message as below:
MINIMIZATION SUCCESSFUL
HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION.
REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY
AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT.
NO. OF FUNCTION EVALUATIONS USED: 968
NO. OF SIG. DIGITS IN FINAL EST.: 3.2
R MATRIX ALGORITHMICALLY NON-POSITIVE-SEMIDEFINITE
BUT NONSINGULAR
R MATRIX IS OUTPUT
COVARIANCE STEP ABORTED
But if I fit the encapsulated drug first and then fit data of both encapsulated
drug and released drug by fixing the typical values of parameters associated
with encapsulated drug compartment, I can get an estimation of standard errors.
And GOF plots look good.
So my question is can I use this stepwise strategy to build up my base model?
If not, what can I do to get standard error estimated in simultaneous fitting?
Any comment or suggestion will be highly appreciated.
Below is my code:
$SUBROUTINES ADVAN9 TRANS1 TOL=3
$MODEL NPAR=8, NCOMP=3, COMP=(CENTRAL,DEFOBS),
COMP=(PERIPH1), COMP=(PERIPH2)
$PK
V1 = THETA(1)*EXP(ETA(1))
V2 = THETA(2)*EXP(ETA(2))
V3 = THETA(3)
VM = THETA(4)
KM = THETA(5)
Q2 = THETA(6)*EXP(ETA(3))
Q3 = THETA(7)
Q4 = THETA(8)
S1 = V1
S2 = V1
$ERROR
R1=0
IF (CMT.EQ.1) R1=1
R2=0
IF (CMT.EQ.2) R2=1
Y1=F+F*EPS(1)
Y2=F+F*EPS(1)
Y=R1*Y1+R2*Y2
IPRED=F
IRES=DV-IPRED
$DES
C1 = A(1)/V1
DADT(1) = - C1*VM/(KM+C1)-Q2*A(1)/V1
DADT(2) = C1*VM/(KM+C1)+Q2*A(1)/V1 + Q4*A(3)/V3- Q3*A(2)/V2-Q4*A(2)/V2
DADT(3) = Q4*A(2)/V2-Q4*A(3)/V3
$THETA (5.27 FIX) (0, 0.5) (0, 3.38) (0.329 FIX) (4.38 FIX) (0.136 FIX) (0,
0.0626) (0, 0.0461)
$OMEGA (0.01) (0.01) (0.01)
$SIGMA (.1)
$EST MAXEVAL=9999 PRINT=5
$COV
$TABLE ID TIME DV AMT RATE CMT NOPRINT FILE=ENCAP4 ONEHEADER
$SCAT (RES WRES) VS TIME BY ID
Best regards,
Huali
Estimation of standard error
Huali,
NONMEM's failure to compute standard errors says nothing about the quality of your model. This has been discussed at length on nmusers. You should look at the parameter estimates for feasibility and use simulation based diagnostics e.g. VPC, to evaluate your model fit. No need to waste time worrying about lack of SEs. Holford's rule -- the covariance step runs with simple models. When the model gets interesting NONMEM will stop calculating SEs.
Nick
Huali Wu wrote:
> Dear NMusers:
>
> I am trying to fit a data set with concentrations of encapsulated drug and released drug. These concentrations were measured after an hour infusion of encapsulated drug. The problem is that I cannot get standard error estimated from simultaneous fitting of all the data. And I got an error message as below: MINIMIZATION SUCCESSFUL
>
> HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION.
> REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY
> AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT.
> NO. OF FUNCTION EVALUATIONS USED: 968
> NO. OF SIG. DIGITS IN FINAL EST.: 3.2
> R MATRIX ALGORITHMICALLY NON-POSITIVE-SEMIDEFINITE
> BUT NONSINGULAR
> R MATRIX IS OUTPUT
> COVARIANCE STEP ABORTED
>
> But if I fit the encapsulated drug first and then fit data of both encapsulated drug and released drug by fixing the typical values of parameters associated with encapsulated drug compartment, I can get an estimation of standard errors. And GOF plots look good. So my question is can I use this stepwise strategy to build up my base model? If not, what can I do to get standard error estimated in simultaneous fitting? Any comment or suggestion will be highly appreciated. Below is my code: $SUBROUTINES ADVAN9 TRANS1 TOL=3
>
> $MODEL NPAR=8, NCOMP=3, COMP=(CENTRAL,DEFOBS),
> COMP=(PERIPH1), COMP=(PERIPH2)
>
> $PK
>
> V1 = THETA(1)*EXP(ETA(1))
> V2 = THETA(2)*EXP(ETA(2))
> V3 = THETA(3)
> VM = THETA(4)
> KM = THETA(5)
> Q2 = THETA(6)*EXP(ETA(3))
> Q3 = THETA(7)
>
> Q4 = THETA(8) S1 = V1
>
> S2 = V1
>
> $ERROR R1=0
>
> IF (CMT.EQ.1) R1=1
> R2=0
> IF (CMT.EQ.2) R2=1
> Y1=F+F*EPS(1)
> Y2=F+F*EPS(1)
> Y=R1*Y1+R2*Y2
> IPRED=F
> IRES=DV-IPRED
>
> $DES
>
> C1 = A(1)/V1
> DADT(1) = - C1*VM/(KM+C1)-Q2*A(1)/V1
>
> DADT(2) = C1*VM/(KM+C1)+Q2*A(1)/V1 + Q4*A(3)/V3- Q3*A(2)/V2-Q4*A(2)/V2 DADT(3) = Q4*A(2)/V2-Q4*A(3)/V3 $THETA (5.27 FIX) (0, 0.5) (0, 3.38) (0.329 FIX) (4.38 FIX) (0.136 FIX) (0, 0.0626) (0, 0.0461)
>
> $OMEGA (0.01) (0.01) (0.01)
> $SIGMA (.1)
>
> $EST MAXEVAL=9999 PRINT=5
> $COV
> $TABLE ID TIME DV AMT RATE CMT NOPRINT FILE=ENCAP4 ONEHEADER
> $SCAT (RES WRES) VS TIME BY ID
>
> Best regards, Huali
--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
[EMAIL PROTECTED] tel:+64(9)373-7599x86730 fax:+64(9)373-7090
www.health.auckland.ac.nz/pharmacology/staff/nholford
Huali and Nick,
Failure of $COV step sometimes tells about errors in the code. In this case, I think the problem is in
S2 = V1
should it be
S2 = V2 ?
Also,
> Y1=F+F*EPS(1)
> Y2=F+F*EPS(1)
> Y=R1*Y1+R2*Y2
results in
Y =F+F*EPS(1)
for both MCT=1 and CMT=2
so R1 / R2 /Y1 /Y2 part can be omitted. Or you can use separate errors for each CMT:
> Y1=F+F*EPS(1)
> Y2=F+F*EPS(2)
> Y=R1*Y1+R2*Y2
--
S2 = V2 should help to get SE. If not, try MATRIX=S on $COV line.
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
Huali Wu wrote:
> Dear NMusers:
>
> I am trying to fit a data set with concentrations of encapsulated drug and released drug. These concentrations were measured after an hour infusion of encapsulated drug. The problem is that I cannot get standard error estimated from simultaneous fitting of all the data. And I got an error message as below: MINIMIZATION SUCCESSFUL
>
> HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION.
> REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY
> AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT.
> NO. OF FUNCTION EVALUATIONS USED: 968
> NO. OF SIG. DIGITS IN FINAL EST.: 3.2
> R MATRIX ALGORITHMICALLY NON-POSITIVE-SEMIDEFINITE
> BUT NONSINGULAR
> R MATRIX IS OUTPUT
> COVARIANCE STEP ABORTED
>
> But if I fit the encapsulated drug first and then fit data of both encapsulated drug and released drug by fixing the typical values of parameters associated with encapsulated drug compartment, I can get an estimation of standard errors. And GOF plots look good. So my question is can I use this stepwise strategy to build up my base model? If not, what can I do to get standard error estimated in simultaneous fitting? Any comment or suggestion will be highly appreciated. Below is my code: $SUBROUTINES ADVAN9 TRANS1 TOL=3
>
> $MODEL NPAR=8, NCOMP=3, COMP=(CENTRAL,DEFOBS),
> COMP=(PERIPH1), COMP=(PERIPH2)
>
> $PK
>
> V1 = THETA(1)*EXP(ETA(1))
> V2 = THETA(2)*EXP(ETA(2))
> V3 = THETA(3)
> VM = THETA(4)
> KM = THETA(5)
> Q2 = THETA(6)*EXP(ETA(3))
> Q3 = THETA(7)
>
> Q4 = THETA(8) S1 = V1
>
> S2 = V1
>
> $ERROR R1=0
>
> IF (CMT.EQ.1) R1=1
> R2=0
> IF (CMT.EQ.2) R2=1
> Y1=F+F*EPS(1)
> Y2=F+F*EPS(1)
> Y=R1*Y1+R2*Y2
> IPRED=F
> IRES=DV-IPRED
>
> $DES
>
> C1 = A(1)/V1
> DADT(1) = - C1*VM/(KM+C1)-Q2*A(1)/V1
>
> DADT(2) = C1*VM/(KM+C1)+Q2*A(1)/V1 + Q4*A(3)/V3- Q3*A(2)/V2-Q4*A(2)/V2 DADT(3) = Q4*A(2)/V2-Q4*A(3)/V3 $THETA (5.27 FIX) (0, 0.5) (0, 3.38) (0.329 FIX) (4.38 FIX) (0.136 FIX) (0, 0.0626) (0, 0.0461)
>
> $OMEGA (0.01) (0.01) (0.01)
> $SIGMA (.1)
>
> $EST MAXEVAL=9999 PRINT=5
> $COV
> $TABLE ID TIME DV AMT RATE CMT NOPRINT FILE=ENCAP4 ONEHEADER
> $SCAT (RES WRES) VS TIME BY ID
>
> Best regards, Huali