RE: COND LAPLACE LIKELIHOOD

From: wangx826
Date: 04 Aug 2009 14:23:12 -0500

Hi Samer,

\$MODEL
COMP=(ABSO)
COMP=(CENT)
COMP=(PERI)
COMP=(EFFECT)
\$PK
CL =ICL*24
V2 = IVC
Q=...
K23=Q/V2
V3=
K32=Q/V3
K=CL/V2
KA=...
KE0 = THETA(6)
B1 = THETA(1)
B2 = THETA(2)
B3 = THETA(3)
EMAX = THETA(4)
EC50 = THETA(5)*EXP(ETA(1))

\$DES

\$ERROR
CE=A(4)
DRUG = EMAX*CE/(EC50+CE)
A0 = B1 + DRUG
A1 = B1 + B2 + DRUG
A2 = B1 + B2 + B3 + DRUG
C0 = EXP(A0)
C1 = EXP(A1)
C2 = EXP(A2)
P0 = C0/(1+C0) ; Probability of Score=>0
P1 = C1/(1+C1) ; Probability of Score=>1
P2 = C2/(1+C2) ; Probability of Score=>2

PR0 = P0 ; Probability of Score=0
PR1 = P1-P0 ; Probability of Score=1
PR2 = P2-P1 ; Probability of Score=2
PR3 = 1-P2 ; Probability of Score=3

IF (DV.EQ.0) Y=PR0
IF (DV.EQ.1) Y=PR1
IF (DV.EQ.2) Y=PR2
IF (DV.EQ.3) Y=PR3

\$THETA (-20 -6.3) ; THETA1 B1
\$THETA (-10 -0.3) ; THETA2 B2
\$THETA (-10 2) ; THETA3 B3
\$THETA (0 5) ; THETA4 EMAX
\$THETA (0 50) ; THETA5 EC50
\$THETA (0 1) ; THETA6 KEO

\$OMEGA 2

\$ESTIMATION MAXEVAL=9999 PRINT=5 METHOD=COND LAPLACIAN LIKELIHOOD
NOABORT MSFO=MSF1

Here I include the dataset for first two subjects. Since it is a
simultaneous PKPD link model, DV in the data set as follows is categorical=

PD data. The dose was given daily and DV was recorded daily as well.
#SUBJ TIME AMT EVID II ADDL PD KA CL V2 MDV
1 0 . 0 . . 0 9.96 4.12 63.57 0
1 0 1 1 1 20 . 9.96 4.12 63.57 1
1 1 . 0 . . 0 9.96 4.12 63.57 0
1 2 . 0 . . 0 9.96 4.12 63.57 0
1 3 . 0 . . 0 9.96 4.12 63.57 0
1 4 . 0 . . 0 9.96 4.12 63.57 0
1 5 . 0 . . 0 9.96 4.12 63.57 0
1 6 . 0 . . 0 9.96 4.12 63.57 0
1 7 . 0 . . 0 9.96 4.12 63.57 0
1 8 . 0 . . 0 9.96 4.12 63.57 0
1 9 . 0 . . 0 9.96 4.12 63.57 0
1 10 . 0 . . 0 9.96 4.12 63.57 0
1 11 . 0 . . 0 9.96 4.12 63.57 0
1 12 . 0 . . 0 9.96 4.12 63.57 0
1 13 . 0 . . 0 9.96 4.12 63.57 0
1 14 . 0 . . 0 9.96 4.12 63.57 0
1 15 . 0 . . 0 9.96 4.12 63.57 0
1 16 . 0 . . 0 9.96 4.12 63.57 0
1 17 . 0 . . 0 9.96 4.12 63.57 0
1 18 . 0 . . 0 9.96 4.12 63.57 0
1 19 . 0 . . 0 9.96 4.12 63.57 0
1 20 . 0 . . 0 9.96 4.12 63.57 0
1 21 . 0 . . 0 9.96 4.12 63.57 0
1 22 . 0 . . 0 9.96 4.12 63.57 0
1 23 . 0 . . 0 9.96 4.12 63.57 0
1 24 . 0 . . 0 9.96 4.12 63.57 0
1 25 . 0 . . 0 9.96 4.12 63.57 0
1 26 . 0 . . 0 9.96 4.12 63.57 0
1 27 . 0 . . 0 9.96 4.12 63.57 0
2 0 . 0 . . 0 1.52 4.68 66.91 0
2 0 2 1 1 20 . 1.52 4.68 66.91 1
2 1 . 0 . . 0 1.52 4.68 66.91 0
2 2 . 0 . . 0 1.52 4.68 66.91 0
2 3 . 0 . . 0 1.52 4.68 66.91 0
2 4 . 0 . . 0 1.52 4.68 66.91 0
2 5 . 0 . . 0 1.52 4.68 66.91 0
2 6 . 0 . . 0 1.52 4.68 66.91 0
2 7 . 0 . . 0 1.52 4.68 66.91 0
2 8 . 0 . . 0 1.52 4.68 66.91 0
2 9 . 0 . . 0 1.52 4.68 66.91 0
2 10 . 0 . . 0 1.52 4.68 66.91 0
2 11 . 0 . . 0 1.52 4.68 66.91 0
2 12 . 0 . . 1 1.52 4.68 66.91 0
2 13 . 0 . . 1 1.52 4.68 66.91 0
2 14 . 0 . . 1 1.52 4.68 66.91 0
2 15 . 0 . . 1 1.52 4.68 66.91 0
2 16 . 0 . . 1 1.52 4.68 66.91 0
2 17 . 0 . . 1 1.52 4.68 66.91 0
2 18 . 0 . . 1 1.52 4.68 66.91 0
2 19 . 0 . . 1 1.52 4.68 66.91 0
2 20 . 0 . . 1 1.52 4.68 66.91 0
2 21 . 0 . . 3 1.52 4.68 66.91 0
2 22 . 0 . . 3 1.52 4.68 66.91 0
2 23 . 0 . . 3 1.52 4.68 66.91 0
2 24 . 0 . . 3 1.52 4.68 66.91 0
2 25 . 0 . . 3 1.52 4.68 66.91 0
2 26 . 0 . . 3 1.52 4.68 66.91 0
2 27 . 0 . . 3 1.52 4.68 66.91 0
2 28 . 0 . . 2 1.52 4.68 66.91 0

Thanks,
Tianli
***************************************************************************=
***
Tianli Wang
University of Minnesota,
Department of Pharmaceutics

On Aug 4 2009, Samer Mouksassi wrote:

>And the proportional odds PD data. The likelihood may go wild if some
>categories are very rare or non esistent. You need to gard against over
>or underflow.
>
>-----Original Message-----
>From: owner-nmusers
>On Behalf Of wangx826
>Sent: 2009-08-04 13:38
>To: NMUSERS
>Subject: [NMusers] COND LAPLACE LIKELIHOOD
>
>Dear nmusers,
>
>Has anybody seen the following error message from NONMEM:
>"CONDITIONAL LIKELIHOOD SET TO NEGATIVE VALUE
> WITH INDIVIDUAL 2 (IN INDIVIDUAL RECORD ORDERING), DATA RECORD 13"?
>What does it mean?
>
>I am trying to use conditional lapalacian likelihood method to generate
>a
>proportional odds model dealing with categorical PD data. Here is a
>little
>piece of my data set:
>SUBJ TIME AMT EVID II ADDL DV CL V
>MDV
>2 0 . 0 . . 0 4.12 63
>0
>2 0 1 1 1 20 . 4.12 63
>1
>2 0.005 . 2 . . . 4.12 63
>1
>2 0.01 . 2 . . . 4.12 63
>1
>.
>.
>.
>2 12 . 0 . . 0 4.12 63
>0
>2 13 . 0 . . 1 4.12 63
>0
>.
>.
>.
>But I don't think the problem exists in the data set because I tried
>deleting the rows indicated in the error message, but the exactly same
>message still came out and NONMEM stopped running. I have no idea what's
>
>the problem for my model.
>