Estimation of standard error
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