RE: Different EBE estimation between original and enriched dataset with MDV=1

-----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Bauer, Robert Sent: Sunday, November 25, 2012 9:11 PM To: Leonid Gibiansky; [email protected] Cc: [email protected] Subject: RE: [NMusers] Different EBE estimation between original and enriched dataset with MDV=1 Pascal: There is one more consideration. If your model depends on the use of covariate data, then during the numerical integration from time t1 to t2, where t1 and t2 are times of two contiguous records, which have values of the covariate c1 and c2, respectively, NONMEM uses the covariate at time t2 (call it c2)during the interval from t>t1 to t<=t2. During your original estimation, your data records were, perhaps, as an example: Time covariate MDV 1.0 1.0 0 1.5 2.0 0 With the filled in data set, perhaps you filled in the covariates as follows: Time covariate MDV 1.0 1.0 0 1.25 1.0 1 1.5 2.0 0 Or perhaps you made an interpolation for the covariate at the inserted time of 1.25, to be 1.5. But NONMEM made the following equivalent interpretation during your original estimation: Time covariate MDV 1.0 1.0 0 1.25 2.0 1 1.5 2.0 0 That is, when the time record 1.25 was not there, it supplied the numerical integrater with the covariate value of 2.0 for all times from >1.0 to <=1.5, as stated earlier. Even though MDV=1 on the inserted records, NONEMM simply does not include the DV of that record in the objective function evaluation, but will still use the other information for simulation, by simulation I mean, for the numerical integration during estimation. In short, your model has changed regarding the covariate pattern based on the expanded data set. By the way, there is a utility program called finedeata, that actually facilitates data record filling, with options on how to fill in covariates, in nonmem7.3 beta. I will send the e-mail to this shortly. If you are not using covariates in the manner I described above, then please ignore my lengthy explanation. Robert J. Bauer, Ph.D. Vice President, Pharmacometrics, R&D ICON Development Solutions 7740 Milestone Parkway Suite 150 Hanover, MD 21076 Tel: (215) 616-6428 Mob: (925) 286-0769 Email: [email protected] Web: www.iconplc.com -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Leonid Gibiansky Sent: Friday, November 23, 2012 12:15 PM To: [email protected] Cc: [email protected] Subject: Re: [NMusers] Different EBE estimation between original and enriched dataset with MDV=1 Hi Pascal, I think the problem is in the precision of the integration routine. With extra points, you change the ODE integration process and the results. I would use TOL=10 or higher in the original estimation. I have seen cases when changing TOL from 6 to 0 or 10 changed the outcome quite significantly. Leonid -------------------------------------- Leonid Gibiansky, Ph.D. President, QuantPharm LLC web: www.quantpharm.com e-mail: LGibiansky at quantpharm.com tel: (301) 767 5566 On 11/23/2012 11:08 AM, [email protected] wrote: > Dear NM-User community, > > I have a model with 2 differential equations and I use ADVAN6 TOL=5. > In $DES, I am using T the continuous time variable. The run converges, > $COV is OK, and the model gives a reasonable fit. In order to compute > some statistics which cannot be obtained analytically, I need to > compute individual predictions based on individual POSTHOC parameters > and an extended grid of time for interpolating the observed times. > > So I have > 1) added to my original dataset extra points regularly spaced with > MDV=1. To give you an idea, my average observation time is 25, with a > range going from 5 to 160. So my grid was set so that I have a dummy > observation every 1 unit of time. > 2) rerun my model using $MSFI to initialize the pop parameters, with > MAXEVAL=0 and POSTHOC options so that individual empirical Bayes > estimates (EBE) parameters for each patient would be first > re-estimated, then the prediction would be computed. > > Then I > 3) checked that my new predictions computed from the extended dataset > match the predictions of the original dataset at observed time points. > I had the surprise to see that for some individuals those predictions > match, for some others they slightly diverge, and for few others they > are dramatically different. I checked the EBEs and they were clearly > different between the original dataset and the one with the dummy points. > 4) I decided to redo the grid with only one dummy point every 1/4 of > time unit. The result was less dramatic, but still for most of my > individuals the EBEs predictions were diverging from the original ones > computed without the dummy times. > > Of course the solution for me is to estimate the EBEs from the > original dataset, export them in a table and reread them to initialize > the parameter of my individuals using only dummy time points and no > observations. > > This problem reminds me something that was discussed previously on > nm-user, but I could not recover the source in the archive. > > Anyway is this something known and predictable that when adding dummy > points with MDV=1 to your original dataset you sometimes get very > different EBEs ? Are there cases/models/ADVAN where the problem is > likely to happen? Is their a way to fix it it in NONMEM other than the > trick I used? > > Thanks for your replies! > > Kind regards, > > Pascal Girard, PhD > [email protected] > Head of Modeling & Simulation - Oncology Global Exploratory Medicine > Merck Serono S.A. * Geneva > Tel: +41.22.414.3549 > Cell: +41.79.508.7898 > > This message and any attachment are confidential and may be privileged > or otherwise protected from disclosure. If you are not the intended > recipient, you must not copy this message or attachment or disclose > the contents to any other person. If you have received this > transmission in error, please notify the sender immediately and delete > the message and any attachment from your system. 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