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

On 11/27/2012 1:59 PM, [email protected] wrote: > Hi Leonid, > > Thanks for the additional suggestion to use ADVAN13. I was able to > increase TOL up to 16, SIGL to 14, but still have the same biases for > the moderate to almost flat initial slope after baseline when using > dummy points spaced every 1 unit of time. When I reduce number of dummy > points with one dummy point every 4 units of time, the bias almost > disappear. > > Kind regards, > > Pascal > > From: Leonid Gibiansky <[email protected]> > To: [email protected] > Cc: "[email protected]" <[email protected]> > Date: 26/11/2012 21:40 > Subject: Re: [NMusers] Different EBE estimation between original and > enriched dataset with MDV=1 > ------------------------------------------------------------------------ > > Hi Pascal, > You may want to switch to ADVAN13. It is much more stable for stiff > problems, and may allow to increase TOL. > Thanks > Leonid > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > On 11/26/2012 2:43 PM, [email protected] wrote: > > Dear All, > > > > Thanks for your detailed response and tricks. I am trying to address > > each of them after several trial and errors with your suggestions: > > > > 1) I have only time-invariant covariates. Buth thanks to Robert and > > Bill for mentioning it. I will remember! > > > > 2) I did not use the EVID=2 for my dummy times. Now I am using them, but > > it does not help. > > > > 3) Starting from non optimized parameters rather than $MSFI as suggested > > by Joachim does not help. But I like your explanation. Nevertheless I > > can't live with "the differences [...] within the range you would also > > find if you did a bootstrap" since those differences change the profiles > > I observe. > > > > 4) The nice trick suggested by Heiner (After the last time point of an > > ID you may add a line with EVID=3 (reset event) with the TIME > > (TIMERESET>the last datapoint of the ID of interest)may work, but would > > probably be too complex to implement for my special dataset since I have > > a long history of not evenly spaced dosing. But thanks, Heine, I will > > also remember this one. > > > > 5) Increasing the TOL is the only thing that improves the prediction. > > Thanks Leonid you are right when you write "the problem is in the > > precision of the integration routine". But with the data I have, I > > cannot increase it beyond 8. By the way, in my model I am estimating the > > initial condition at baseline in one of my compartment using a random > > effect. When the slope after the baseline is large, I got almost no > > bias. But when it is a moderate slope, the bias prediction with dummy > > points appears and is increasing when the slope is decreasing. This > > probably confirms the issue of the precision with integration routine. > > > > 6) The only solution which I mention in in my 1st Email and that was > > also suggested by Jean Lavigne : one separate run for the estimation of > > the EBEs and one from the simulation on dummy time points. > > > > 7.2) Thanks Robert. I am glad to learn that in 7.3 there will be an > > option to automatically "fill in extra records with small time > > increments, to provide smooth plots". I imagine that using this > > utility program will not change the precision of the integration routine > > since it will be build in. I will just have to wait a little bit for > > getting access to it. > > > > Kind regards, > > > > Pascal > > > > PS > > As someone who used to live by the Lake Leman would have said, NONMEM, > > sometimes, "It's a kind og magic!" :-) > > > > > > > > From: Herbert Struemper <[email protected]> > > To: "[email protected]" <[email protected]> > > Date: 26/11/2012 16:13 > > Subject: RE: [NMusers] Different EBE estimation between original and > > enriched dataset with MDV=1 > > Sent by: [email protected] > > ------------------------------------------------------------------------ > > > > > > > > Pascal, > > I had the same issue a while ago with time-invariant covariates. Back > > then with NM6.2, adding an EVID column to the data set and setting > > EVID=2 for additional records preserved the ETAs of the original > > estimation (while only setting MDV=1 for additional records did not). > > Herbert > > > > Herbert Struemper, Ph.D. > > Clinical Pharmacology, Modeling & Simulation > > GlaxoSmithKline, RTP, 17.2230.2B > > Tel.: 919.483.7762 (GSK-Internal: 7/8-703.7762) > > > > > > > > -----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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