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

From: [email protected] [mailto:[email protected]] On Behalf Of [email protected] Sent: 23 November 2012 16:09 To: [email protected] Subject: [NMusers] Different EBE estimation between original and enriched dataset with MDV=1 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. Merck KGaA, Darmstadt, Germany and any of its subsidiaries do not accept liability for any omissions or errors in this message which may arise as a result of E-Mail-transmission or for damages resulting from any unauthorized changes of the content of this message and any attachment thereto. Merck KGaA, Darmstadt, Germany and any of its subsidiaries do not guarantee that this message is free of viruses and does not accept liability for any damages caused by any virus transmitted therewith. Click http://www.merckgroup.com/disclaimer to access the German, French, Spanish and Portuguese versions of this disclaimer.

From: [email protected] [mailto:[email protected]] On Behalf Of [email protected] Sent: Friday, November 23, 2012 11:09 AM To: [email protected] Subject: [NMusers] Different EBE estimation between original and enriched dataset with MDV=1 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]<mailto:[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. Merck KGaA, Darmstadt, Germany and any of its subsidiaries do not accept liability for any omissions or errors in this message which may arise as a result of E-Mail-transmission or for damages resulting from any unauthorized changes of the content of this message and any attachment thereto. Merck KGaA, Darmstadt, Germany and any of its subsidiaries do not guarantee that this message is free of viruses and does not accept liability for any damages caused by any virus transmitted therewith. Click http://www.merckgroup.com/disclaimer to access the German, French, Spanish and Portuguese versions of this disclaimer. This electronic transmission may contain confidential and/or proprietary information and is intended to be for the use of the individual or entity named above. If you are not the intended recipient, be aware that any disclosure, copying, distribution or use of the contents of this electronic transmission is prohibited. If you have received this electronic transmission in error, please destroy it and immediately notify us of the error. Thank you.

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. Merck KGaA, Darmstadt, Germany and any > of its subsidiaries do not accept liability for any omissions or errors > in this message which may arise as a result of E-Mail-transmission or > for damages resulting from any unauthorized changes of the content of > this message and any attachment thereto. Merck KGaA, Darmstadt, Germany > and any of its subsidiaries do not guarantee that this message is free > of viruses and does not accept liability for any damages caused by any > virus transmitted therewith. > > Click _ http://www.merckgroup.com/disclaimer_to access the German, > French, Spanish and Portuguese versions of this disclaimer.

On Nov 23, 2012, at 12:57 PM, "Leonid Gibiansky" <[email protected]> wrote: > 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. Merck KGaA, Darmstadt, Germany and any >> of its subsidiaries do not accept liability for any omissions or errors >> in this message which may arise as a result of E-Mail-transmission or >> for damages resulting from any unauthorized changes of the content of >> this message and any attachment thereto. Merck KGaA, Darmstadt, Germany >> and any of its subsidiaries do not guarantee that this message is free >> of viruses and does not accept liability for any damages caused by any >> virus transmitted therewith. >> >> Click _ http://www.merckgroup.com/disclaimer_to access the German, >> French, Spanish and Portuguese versions of this disclaimer.

-----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Denney, William S. Sent: Friday, November 23, 2012 2:06 PM To: Leonid Gibiansky Cc: [email protected]; [email protected] Subject: Re: [NMusers] Different EBE estimation between original and enriched dataset with MDV=1 Hi Pascal, In addition to Leonid's answer, if you have time-varying covariates and aren't explicitly computing the current value in the $DES block and are interpolating them (with something other than LOCF), that could explain the difference. The reason would be that NONMEM only resets the value at a new data row, so those new MDV rows would modify the interpolation. It could also explain the difference between the individuals if some have larger or smaller changes in the time-variant covariate. Thanks, Bill On Nov 23, 2012, at 12:57 PM, "Leonid Gibiansky" <[email protected]> wrote: > 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. Merck KGaA, Darmstadt, Germany and any >> of its subsidiaries do not accept liability for any omissions or errors >> in this message which may arise as a result of E-Mail-transmission or >> for damages resulting from any unauthorized changes of the content of >> this message and any attachment thereto. Merck KGaA, Darmstadt, Germany >> and any of its subsidiaries do not guarantee that this message is free >> of viruses and does not accept liability for any damages caused by any >> virus transmitted therewith. >> >> Click _ http://www.merckgroup.com/disclaimer_to access the German, >> French, Spanish and Portuguese versions of this disclaimer.

-----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. Merck KGaA, > Darmstadt, Germany and any of its subsidiaries do not accept liability > for any omissions or errors in this message which may arise as a > result of E-Mail-transmission or for damages resulting from any > unauthorized changes of the content of this message and any attachment > thereto. Merck KGaA, Darmstadt, Germany and any of its subsidiaries do > not guarantee that this message is free of viruses and does not accept > liability for any damages caused by any virus transmitted therewith. > > Click _ http://www.merckgroup.com/disclaimer_to access the German, > French, Spanish and Portuguese versions of this disclaimer.

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. Merck KGaA, > Darmstadt, Germany and any of its subsidiaries do not accept liability > for any omissions or errors in this message which may arise as a > result of E-Mail-transmission or for damages resulting from any > unauthorized changes of the content of this message and any attachment > thereto. Merck KGaA, Darmstadt, Germany and any of its subsidiaries do > not guarantee that this message is free of viruses and does not accept > liability for any damages caused by any virus transmitted therewith. > > Click _ http://www.merckgroup.com/disclaimer_to access the German, > French, Spanish and Portuguese versions of this disclaimer.

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. Merck KGaA, > > Darmstadt, Germany and any of its subsidiaries do not accept liability > > for any omissions or errors in this message which may arise as a > > result of E-Mail-transmission or for damages resulting from any > > unauthorized changes of the content of this message and any attachment > > thereto. Merck KGaA, Darmstadt, Germany and any of its subsidiaries do > > not guarantee that this message is free of viruses and does not accept > > liability for any damages caused by any virus transmitted therewith. > > > > Click _ http://www.merckgroup.com/disclaimer_to access the German, > > French, Spanish and Portuguese versions of this disclaimer. >

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. Merck KGaA, > > Darmstadt, Germany and any of its subsidiaries do not accept liability > > for any omissions or errors in this message which may arise as a > > result of E-Mail-transmission or for damages resulting from any > > unauthorized changes of the content of this message and any attachment > > thereto. Merck KGaA, Darmstadt, Germany and any of its subsidiaries do > > not guarantee that this message is free of viruses and does not accept > > liability for any damages caused by any virus transmitted therewith. > > > > Click _ http://www.merckgroup.com/disclaimer_to access the German, > > French, Spanish and Portuguese versions of this disclaimer. >

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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