RE: COND LAPLACE LIKELIHOOD

-----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of [email protected] Sent: Tuesday, August 04, 2009 11:38 PM To: Leonid Gibiansky Cc: Samer Mouksassi; NMUSERS Subject: Re: [NMusers] COND LAPLACE LIKELIHOOD Leonid, Thanks for your suggestion. I totally agree the part you indicated was incorrect in my original control stream. But after I revised it, NONMEM still stopped running and gave the same error message like " CONDITIONAL LIKELIHOOD SET TO NEGATIVE VALUE WITH INDIVIDUAL 2 (IN INDIVIDUAL RECORD ORDERING), DATA RECORD 13". Is there anything else wrong in my coding? I would appreciate pretty much if you or some one else could point it out for me. Thanks again, Tianli On Aug 4 2009, Leonid Gibiansky wrote: >something is incorrect here, either model or comments: > P0 = C0/(1+C0) ; Probability of Score=>0 > P1 = C1/(1+C1) ; Probability of Score=>1 > P2 = C2/(1+C2) ; Probability of Score=>2 > >Probability of Score=>0 is always 1 (because score is 0 or positive >number as I can see). > >My guess is that the model is incorrect, it should be (assuming that the >drug increases probability of higher scores while natural state favors >score 0) > > > A1 = B1 + DRUG > > A2 = B1 - B2 + DRUG > > A3 = B1 - B2 - B3 + DRUG > > > C1 = EXP(A1) > > C2 = EXP(A2) > > C3 = EXP(A3) > > P1 = C1/(1+C1) ; Probability of Score=>1 > > P2 = C2/(1+C2) ; Probability of Score=>2 > > P3 = C3/(1+C3) ; Probability of Score=>3 > > > > PR0 = 1-P1 ; Probability of Score=0 > > PR1 = P1-P2 ; Probability of Score=1 > > PR2 = P2-P3 ; Probability of Score=2 > > PR3 = P3 ; 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 > > B2 and B3 should be positive > >-------------------------------------- >Leonid Gibiansky, Ph.D. >President, QuantPharm LLC >web: www.quantpharm.com >e-mail: LGibiansky at quantpharm.com >tel: (301) 767 5566 > > > > >[email protected] wrote: >> Hi Samer, >> Thanks for your quick reply. Here is my control stream: >> >> $SUBS ADVAN6 TOL=6 >> $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 >> DADT(1)=-A(1)*KA >> DADT(2)=A(1)*KA-A(2)*K23+A(3)*K32-A(2)*K >> DADT(3)=A(2)*K23-A(3)*K32 >> DADT(4)=KE0*(A(2)/V2-A(4)) >> >> $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: >> >>> Can you include your control (or at least your $EST bloc) >>> 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: [email protected] [mailto:[email protected]] >>> On Behalf Of [email protected] >>> 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. >>> >>> Thanks in advance, >>> >>> Tianli >>> >> >> >