From:"Luciane Velasque"Subject:[NMusers] large CV Date:Tue, 5 Mar 2002 10:25:56 -0300 Hi ! I am modeling the population pharmacokinetics of didanosine. I have 72 voluntaries who took one oral dose of 200 mg. Thirteen blood samples were obtained in the times 0, 0.25, 0.5, 0.75, 1.0, 1.5, 2, 2.5, 3, 4, 6, 8 e 10 . the CONTROL STATEMENTS is : ...... $SUBROUTINES ADVAN4 TRANS4 $PK TVCL=THETA(1) CL=TVCL*EXP(ETA(1)) TV2=THETA(2) V2=TV2+ETA(2) TVQ=THETA(3) Q=TVQ*EXP(ETA(3)) TVV3=THETA(4) V3=TVV3*EXP(ETA(4)) TKA=THETA(5) KA=TVKA*EXP(ETA(5)) S2=V2 S3=V3 K=CL/V2 K23=Q/V2 K32=Q/V3 KA=KA $ERROR Y = F + F*ERR(1) $THETA (0,120)(0,5)(0,30)(0,50) $OMEGA 1 1 1 1 $SIGMA 1 $ESTIMATION MAXEVAL=9999 SIGDIGITS=2 POSTHOC $COVARIANCE The estimated OMEGA(V2) is very large and the CV = SQRT(OMEGA(V2))*100 = 1600 %. Why is this happening ? Thanks in advance. Luciane _______________________________________________________ From:"Bachman, William" Subject:RE: [NMusers] large CV Date:Wed, 5 Mar 2003 08:50:21 -0500 A couple of possibilities: 1. interindividual variability in V2 may actually be large 2. you might not have the information in your data to determine the interindividual variability in V2 (interindividual variability in the peripheral parameters is often poorly determined, you may want to omit the eta's on V2 and Q) 3. with dense data and high variability, use of FOCE method is recommended (METHOD=1) 4. also, use INTERACTION with METHOD=1 if possible 5. unless you have observations in the peripheral compartment, you don't need S3=V3 (but won't have an effect on your model if you leave it in). William J. Bachman, Ph.D. GloboMax LLC 7250 Parkway Drive, Suite 430 Hanover, MD 21076 410-782-2212 bachmanw@globomax.com _______________________________________________________ From:"Farrell, Colm" Subject:RE: [NMusers] large CV Date:Wed, 5 Mar 2003 14:10:22 -0000 The control stream shows an additive error structure for IIV on V2, whereas the calculation of the associated CV is for proportional/exponential error structure. Colm Farrell GloboMax LLC _______________________________________________________ From:"Kowalski, Ken" Subject:RE: [NMusers] large CV Date:Wed, 5 Mar 2003 09:12:46 -0500 Luciane, FO often estimates large CVs with dense data. You should try FOCE and since you are using a constant CV residual error model you should also use the interaction option (i.e., use METHOD=1 INTERACTION on the $EST statement). Good luck. Ken _______________________________________________________ From:"atul" Subject:Re: [NMusers] large CV Date: Wed, 5 Mar 2003 09:26:09 -0800 Hello Lucaine Try FOCE. It will result in much better estimates. Also look at the correlations between different estimates and include them in the model. Venkatesh Atul Bhattaram Post-doctoral Fellow University of Florida Gainesville-32610 _______________________________________________________ From:"Howard Lee" Subject: RE: [NMusers] large CV Date: Wed, 5 Mar 2003 09:23:15 -0500 Dear Luciane, You modeled `additive' interindividual variability for V2 as the following: TV2=THETA(2) V2=TV2+ETA(2) Therefore, SQRT(OMEGA(V2)) is SD, and your CV (%) is [SD/THETA(2)]*100 = [SQRT(OMEGA(V2))/THETA(2)]*100, which I think will give you smaller CV for V2. Hope this helps. Thank you. Howard Howard Lee, MD, PhD Assistant Professor Center for Drug Development Science Department of Pharmacology, Georgetown University School of Medicine, Box 571441 Washington, DC 20057-1441 USA Tel: 202-687-8198 Fax: 202-687-0193 _______________________________________________________ From:Iñaki Fernández de Trocóniz Subject:Re: [NMusers] large CV Date: Wed, 05 Mar 2003 15:30:27 +0100 Dear Luciane, V2 has been coded as TV2+ETA(2) instead of TV2*EXP(ETA(2)), therefore the expression you are using to compute the CV is not appropriate; in your case the units of the variance depend on the units of the parameter. In addition, you have non-sparse data, perhaps you might consider the use of the FOCE with INTERACTION estimation method. It is possible that in that case the estimates of inter-subject variability are more realistic. Best regards, Iñaki. _______________________________________________________ From:"Sam Liao" Subject: RE: [NMusers] large CV Date:Wed, 5 Mar 2003 09:25:42 -0500 Dear Luciane: two more possibilities, 1) You don't have initial estimates in theta and omega for KA. 2) It may be quite possible that it converge to a local minimum. How is the diagnostic plots look like? In my experience, the inital estimates for two-compartment model are very critical. Best regards, Sam Liao, Ph.D. PharMax Research PO Box 1809, 20 Second Street, Jersey City, NJ 07302 phone: 201-7983202 efax: 1-720-2946783 _______________________________________________________ From:Chuanpu Hu Subject: RE: [NMusers] large CV Date:Wed, 5 Mar 2003 09:44:25 -0500: Luciane, You used lognormal distribution for every parameter except V2, where you used additive normal. In that case, SQRT(OMEGA(V2))*100 depends on the unit ov V2 and is not the CV of V2. You may want to rethink that model. Chuanpu -------------------------------------------------------------------------- Chuanpu Hu, Ph.D. Research Modeling and Simulation Clinical Pharmacology Discovery Medicine GlaxoSmithKline P.O. Box 13398 Five Moore Drive Research Triangle Park, NC 27709 Tel: 919-483-8205 Fax: 919-483-6380 -------------------------------------------------------------------------- _______________________________________________________ From:"Alice I Nichols" Subject: RE: [NMusers] large CV Date:Wed, 5 Mar 2003 10:34:26 -0800 Dear Lucianne, Your model may be overspecified given the available data. I was interested in adding that an essential step in this process is to check the standard error for this parameter (eta2) to see if the estimate you obtain is meaningful. Please look over the SEs you get for all your parameters. If you have a parameter with a SE that is very large and the estimated parameter is having minimal impact on model you may need to drop this parameter from your model. Alice Alice Nichols, PhD Hawthorne Research and Consulting, INC 132 Hawthorne Rd King of Prussia, PA 19406 PH:610-878-9112 / FX:610-878-9113 nichols@bellatlantic.net _______________________________________________________ From:"Luciane Velasque" Subject: Re: [NMusers] large CV Date: Tue, 5 Mar 2002 12:21:02 -0300 When I use FOCE I obtain the message : 0MINIMIZATION SUCCESSFUL NO. OF FUNCTION EVALUATIONS USED: 360 NO. OF SIG. DIGITS IN FINAL EST.: 2.3 ETABAR IS THE ARITHMETIC MEAN OF THE ETA-ESTIMATES, AND THE P-VALUE IS GIVEN FOR THE NULL HYPOTHESIS THAT THE TRUE MEAN IS 0. ETABAR: -.18E-03 .14E-05 -.26E-07 -.46E-06 .87E-03 P VAL.: .10E+01 .94E+00 .72E+00 .80E+00 .92E+00 0R MATRIX ALGORITHMICALLY NON-POSITIVE-SEMIDEFINITE BUT NONSINGULAR 0COVARIANCE STEP ABORTED What should make ? thanks P.S. In the Statment Control V2= TV2*EXP(ETA2) _______________________________________________________ From:"Bachman, William" Subject: RE: [NMusers] large CV Date: Wed, 5 Mar 2003 10:37:02 -0500 Your covariance step simply aborted for the reason stated (see Manual V, p.145). What would I do? The first thing I would do is look at the omega estimates (the interindividual variance estimates), if any are very small (approaching zero, e.g. E-05) or very large, I would remove them from the model because they are poorly estimated. William J. Bachman, Ph.D. GloboMax LLC 7250 Parkway Drive, Suite 430 Hanover, MD 21076 410-782-2212 bachmanw@globomax.com _______________________________________________________ From:"Venkatesh Atul Bhattaram" Subject: Re: [NMusers] large CV Date:Wed, 5 Mar 2003 11:08:40 -0500 Hello Lucaine Clearly some of the estimates of etas are bad. You might want to fix them to zero or to any previously reported value if the subjects belong to the same population and not to any special population. Also I would explore the plots of different etas and try to reduce the "dimensionality" of the model. You will find that when you plot the etas and do a 90 degree projection, most of the variability will be explained by one of the two parameters and you will also notice the scale differences. So it looks like simplification of the model will be a good solution either in terms of fixing or introducing correlations for which you might need some explanations in terms of covariate information. Venkatesh Atul Bhattaram Post-doctoral Fellow University of Florida Gainesville-32610 _______________________________________________________ From:VPIOTROV@PRDBE.jnj.com Subject:RE: [NMusers] large CV Date:Thu, 6 Mar 2003 11:20:59 +0100 When selecting ETAs to be excluded, I would not recommend to rely on omega estimates. This way you could exclude a random effect, which would be essential. Better way (although not ideal, too) is to run sequentially a series of reduced models with each ETA excluded one at a time and then compare MOF values. ETAs associated with an insignificant increase of MOF can be safely excluded. Best regards, Vladimir _______________________________________________________