[NMusers] help with indirect reponse model

[Zheng Lu]

 Dear all:

 With the following code and data, the predicted fit is poor and no
standard error output for parameter. I highly appreciate your kind
help. Thanks a lot in your busy time.

 $PROB PLK INDIRECT RESPONSE MODEL
$INPUT ID TIME DV AMT CMT
$DATA PLKDOSE10SIMUL.CSV IGNORE=C
$SUBROUTINE ADVAN6 TOL=5

 $MODEL
COMP=(CENTRAL,DEFDOSE)
COMP=PERIPH
COMP=EFFECT

 $PK
K10=THETA(1)*EXP(ETA(1))
V1=THETA(2)*EXP(ETA(2))
K12=THETA(3)*EXP(ETA(3))
K21=THETA(4)*EXP(ETA(4))
SYNTH=THETA(5)*EXP(ETA(5))
LOSS=SYNTH
IC50=THETA(6)*EXP(ETA(6))
S1=V1

 $DES
DADT(1)=K21*A(2)-(K12+K10)*A(1)
DADT(2)=K12*A(1)-K21*A(2)
INH=IC50/(IC50+A(1)/S1)
DADT(3)=SYNTH-LOSS*INH*A(3)

 $ERROR
CP=A(1)/S1
Q=1
IF (CMT .EQ. 2) Q=0
Y=Q*F*EXP(ERR(1))+(1-Q)*F*EXP(ERR(2))
IPRE=Y

 $THETA
(0,1.3)   ;K10
(0,3)     ;S1
(0,0.3)   ;K12
(0,0.37)  ;K21
(0,1)     ;SYNTH
(0,10)    ;IC50

$OMEGA
0 FIX
0 FIX
0 FIX
0 FIX
0 FIX
0 FIX

 $SIGMA
0.04
0.5

 $ESTIMATION MAXEVAL=9999 POSTHOC PRINT=5
$COV
$TABLE NOPRINT FILE=PLKPKPD.txt ONEHEADER ID TIME AMT CMT CP IPRE

        CID             TIME            DV              AMT             CMT
        1               0               0               16807           1
        1               0               0               1               3
        1               0               5               0               3
        1               0.0833          4403            0               1
        1               0.0833          5.82            0               3
        1               0.5             2007            0               1
        1               0.5             5.23            0               3
        1               1               948             0               1
        1               1               8.1             0               3
        1               2               323             0               1
        1               2               8.99            0               3
        1               8               27              0               1
        1               8               15.86           0               3
        1               16              3               0               1
        1               16              33.39           0               3
        1               24              33.75           0               3
        1               48              24.19           0               3
        1               72              10.43           0               3
        1               96              8.03            0               3

lu