Hi Pete, I think the standard model most of us would use is baseline as a parameter in the model, and like other parameters it would have a variability between subjects. What you describe sounds like a covariance between the baseline parameter and some other parameter. We are used to include such covariances and I would suggest that to be the primary way to address it. If the distribution of baseline is truncated due to inclusion criteria or physiological limits, I suggest that you may use a semi-parametric IIV model (Peterson et al., Pharm Res. 2009 Sep;26(9):2174-85) If the relation is more complex than easily handled via a covariance, then I suggest that you use the baseline observation as a covariate with error (Dansirikul et al Pharmacokinet Pharmacodyn. 2008 Jun;35(3):269-83.)
Best regards, Mats Mats Karlsson, PhD Professor of Pharmacometrics Dept of Pharmaceutical Biosciences Uppsala University Swedent regards, Mats Postal address: Box 591, 751 24 Uppsala, Sweden Phone +46 18 4714105 Fax + 46 18 4714003 -----Original Message----- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Peter Bonate Sent: Wednesday, August 11, 2010 5:22 PM To: 'nmusers@globomaxnm.com' Subject: [NMusers] Baseline as a covariate I'd like to get the group's opinion on something. I have a pharmacodynamic model and the baseline was shown to be a covariate on one of the model parameters. I was hoping to get general thoughts on the use of the baseline as a covariate. Is there a preference for using the observed baseline vs. NONMEM predicted baseline? And does your opinion change if you have a large residual error? Thanks Pete bonate Peter L. Bonate, PhD, FCP, FAAPS GlaxoSmithKline Clinical Pharmacology, Modeling, and Simulation 5 Moore Drive, 17.2259 Research Triangle Park, NC 27709 phone: 919-483-7534 fax: 919-483-8948 email: peter.l.bon...@gsk.com This is a long one but worth it... Scientists will never make as much money as business executives. There is now mathematical proof for this statement. Postulate 1: Knowledge is power postulate 2: Time is money According to the laws of physics, Work -------- = Power (Eq. 1) Time Since, from the 2 postulates above, Knowledge = Power and Time = Money, we can substitute into the equation 1 and come up with Work -------- = Knowledge Money Solving for money, Work ------------ = Money Knowledge Hence, there are 2 ways to make money. If we slug our guts out working, we can improve our money situation. This is the normal scientific approach. However the suits usually opt for the easier solution, i.e., the less you know, the more you'll make, regardless of the amount of work done. -----Original Message----- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Leonid Gibiansky Sent: Wednesday, August 11, 2010 9:01 AM To: Ann Rigby-Jones Cc: 'nmusers@globomaxnm.com' Subject: Re: [NMusers] Rounding errors with TRANSIT model Ann Try ADVAN6 or ADVAN8 or ADVAN13 . Also, TOL=3 is too small. Increase it to 6-7 or even 9, and also change to NSIG=3 SIGL=9 ($EST step, as recommended in the guide). Also, at least initially, I would remove ETAs from the third compartment and from one of the parameters (Q2 or V2) of the second compartment. Do you really need both ALAG and transit compartment? They serve the same goal, to delay the input, so could be strongly correlated; you may see it on the POSTHOC plot of ETA7-ETA8 versus ETA9. I would try to remove ETAs from ALAG or KTR to check whether they are needed. If nothing helps, use UNCONDITIONAL on the $COV to get standard errors, and if they are reasonable, ignore the error, it may disappear on the next steps of the modeling process Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 On 8/11/2010 5:57 AM, Ann Rigby-Jones wrote: > Dear All > > I'm trying to evaluate a transit model approach in an attempt to better > describe early drug concentrations following intravenous injection (1 > minute and 10 minute infusions) of a sedative-hypnotic drug. However, > every minimisation attempt is terminated due to rounding errors (E=134). > I've tried the usual strategies to overcome this e.g using final > estimates of a terminated run as initial estimates for the next, > changing number of sig dig requested, changing from a diagonal omega to > a block omega, but nothing has been successful. > > I'd be very grateful for any suggestions of what I might try next J > Standard 3-comp and 4-comp mamillary models minise successful with these > data, but so far no luck with any of the transit models, I have tried > model with 1 to 6 transit compartments. An example control stream is > shown below. I'm using NONMEM 7 with Intel Fortran. > > With thanks and all best wishes > > Ann > > $PROBLEM 3comp 2 transit > > $INPUT ID DOSE AMT RATE DUR TIME ORI DV EVID ART AGE WGT > > $DATA PKcen12LN.csv IGNORE=# > > $SUBROUTINES ADVAN9 TOL=3 > > $MODEL > > ;NCOMPS=9 > > COMP(CENTRAL, DEFOBS) ;1 > > COMP(PERIPH1) ;2 > > COMP(PERIPH2) ;3 > > COMP(TRANS1, DEFDOSE) ;4 > > COMP(TRANS2) ;5 > > ;COMP(TRANS3) ;6 > > ;COMP(TRANS4) ;7 > > ;COMP(TRANS5) ;8 > > ;COMP(TRANS6) ;9 > > $PK > > CL=THETA(1)*EXP(ETA(1)) > > Q2=THETA(2)*EXP(ETA(2)) > > Q3=THETA(3)*EXP(ETA(3)) > > V1=THETA(4)*EXP(ETA(4)) > > V2=THETA(5)*EXP(ETA(5)) > > V3=THETA(6)*EXP(ETA(6)) > > K10=CL/V1 > > K12=Q2/V1 > > K13=Q3/V1 > > K21=Q2/V2 > > K31=Q3/V3 > > S1=V1 > > IF (DUR.EQ.10) THEN > > ALAG4=THETA(7)*EXP(ETA(7)) > > ELSE > > ALAG4=THETA(8)*EXP(ETA(8)) > > ENDIF > > KTR=THETA(9)*EXP(ETA(9)) > > $DES > > ;DADT(1)=A(2)*K21 + A(3)*K31 - A(1)*(K10+K12+K13) > > ;DADT(2)=A(1)*K12 - A(2)*K21 > > ;DADT(3)=A(1)*K13 - A(3)*K31 > > DADT(1)=A(5)*KTR + A(2)*K21 + A(3)*K31 - A(1)*(K10+K12+K13) > > DADT(2)=A(1)*K12 - A(2)*K21 > > DADT(3)=A(1)*K13 - A(3)*K31 > > DADT(4)=-A(4)*KTR > > DADT(5)=A(4)*KTR - A(5)*KTR > > ;DADT(6)=A(5)*KTR - A(6)*KTR > > ;DADT(7)=A(6)*KTR - A(7)*KTR > > ;DADT(8)=A(7)*KTR - A(8)*KTR > > ;DADT(9)=A(8)*KTR - A(9)*KTR > > $ERROR > > W=1 > > IPRED= -2 > > IF (F.GT.0) IPRED=LOG(F) > > Y=IPRED + ERR(1) > > IRES=DV-IPRED > > IWRES=IRES/W > > $THETA (0, 725) ;CL > > $THETA (0, 238) ;Q2 > > $THETA (0, 2920) ;Q3 > > $THETA (0, 311) ;V1 > > $THETA (0, 38700) ;V2 > > $THETA (0, 39500) ;V3 > > $THETA (0.2167, 0.522,1) ;ALAG 10MIN > > $THETA (0.00833, 0.122, 1) ;ALAG 1MIN > > $THETA (0, 0.720) ;KTR > > $OMEGA BLOCK(6) > > 0.0959 ; ETA CL > > 0.00804 0.0211 ; ETA Q2 > > 0.00631 0.00143 0.166 ; ETA Q3 > > -0.00506 0.00121 -0.0635 0.0491 ; ETA V1 > > 0.00293 -0.00746 0.00708 -0.00315 0.0328 ; ETA V2 > > 0.00836 0.00688 0.00144 -0.00497 0.00741 0.153 ; ETA V3 > > $OMEGA 0.217 ; ETA ALAG 10 > > $OMEGA 0.4 ; ETA ALAG 1 > > $OMEGA 0.0171 ; ETA KTR > > ;$OMEGA (0.0686) ; ETA CL > > ;$OMEGA (0.01) ; ETA Q2 > > ;$OMEGA (0.0251) ; ETA Q3 > > ;$OMEGA (0 FIX) ; ETA V1 > > ;$OMEGA (0.0328) ; ETA V2 > > ;$OMEGA (0 FIX) ; ETA V3 > > ;$OMEGA (0.170) ; ETA ALAG10 > > ;$OMEGA (1.37) ; ETA ALAG1 > > ;$OMEGA (0.00409) ; ETA KTR > > $SIGMA (0.0944) > > $ESTIMATION METHOD=1 PRINT=1 MAX=9999 NOABORT SIG=3 ;POSTHOC INTER > > MSFO=msfo.outputfile > > ;$COVA > > $TABLE ID EVID AMT TIME IPRED IRES > > NOPRINT FILE=AllRecords.txt > > $TABLE ID > > CL Q2 Q3 V1 V2 V3 > > ETA1 ETA2 ETA3 ETA4 ETA5 ETA6 ETA7 ;ETA8 > > FIRSTONLY NOPRINT NOAPPEND FILE=FirstRecords.txt > > Ann > > _______________________________________________________________________ > > *Ann Rigby-Jones PhD MRSC* > Research Fellow in Pharmacokinetics & Pharmacodynamics > > Peninsula College of Medicine & Dentistry > > N31, ITTC Phase 1 > Tamar Science Park > 1 Davy Road > Derriford > Plymouth > PL6 8BX > > Tel: +44 (0) 1752 432014 > _______________________________________________________________________ >
