Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis

From: Ribbing, Jakob Sent: 18 February 2013 09:59 To: "Kågedal, Matts"; [email protected] Cc: Ribbing, Jakob Subject: RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi Matts, I think you are correct; the problem you describe has not had much (public) discussion. It is also correct like you say that this is mostly a problem in case of all of the below · sequential PK-PD analysis is applied (IPP approach, Zang et al) · non-ignorable degree of shrinkage in PK parameters of relevance o Of relevance: the PK parameters effectively driving PD for the mechanism, e.g. CL/F if AUC is driving. In addition, if PD response develops over several weeks/months then shrinkage in IOV may be ignored even for relevant PK parameters · I would also like to add that for this to be an issue individual PK parameters must explain a fair degree of the variability in PD, which is not always the case o If driving PD with typical PK parameters (along with dose and other PD covariates) does not increase PD omegas, compared to IPP, then either PK shrinkage is already massive, or else it is not an issue for the IPP-PD model If only the IPP approach is possible/practical a simplistic approach to simulate PD data is as follows: · sample (with replacement) the individual PK parameters along with any potential covariates (maintaining correlation between IIP and covariates, i.e. whole subject vectors for these entities, but generally not for dose since generally should only have only random association with IPP or PK/PD-covariates) · then use the re-sampled datasets for simulating PD according to the PD model (driven by IPP, covariates, dose, etc). The degree of shrinkage is then the same for PD estimation and simulation. This approach may for example allow to simulate realistic PD response at multiple dosing, based on only single dose PD. When the MD data becomes available then one may find that variabilities shift between PK and PD due to different PK shrinkage, but I would argue the simulated PD responses still were realistic. This approach is useful for predictions into the same population (especially if sufficient number of subjects available for re-sampling), but may not allow extrapolation into other populations where PK is projected to be different. When possible the obvious solution is to apply one of the alternative approaches to simultaneous PK-PD fit; after you have arrived at a final-IPP model. If a simultaneous fit is obtainable/practical this is the best option, but notice that e.g. if you have rich PK data in healthy and no PK data in patients (plus PD data in both populations): You can estimate separate omegas for PD parameters in healthy vs. patients, but it may be difficult to tell whether patients higher PD variability is due to PK shrinkage, or due to the actual PD variability being higher in this population (or both). PD variability may be confounded by a number of other factors that are actually variability in PK (fu, active metabolites and bio phase distribution, just to mention a few where information may be absent on the individual level). Depending on the purpose of the modelling this often not an issue, however. As you suggest there may be rare situations with IPP where a more complicated approach is needed, with a) simulation and re-estimation of PK model, to obtain Empirical-Bayes Estimates based on simulated data, and then feed these into the subsequent PD model. I would see this as a last resort. There are pitfalls in that if PD parameters have been estimated under one degree of PK shrinkage, then applying these estimates to a simulated example with different PK shrinkage requires adjustment of PD variability. I am not sure anyone has had to go down that route before and if not I hope you do not have to either. Maybe others can advice on this? Best regards Jakob Two methodological references: Simultaneous vs. sequential analysis for population PK/PD data II: robustness of methods. Zhang L, Beal SL, Sheiner LB. J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance. Zhang L, Beal SL, Sheiner LB. J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404.

On 2/18/2013 2:50 AM, "Kågedal, Matts" wrote: > Hi nonmem users, > > I have a question related to shrinkage in PK when doing a sequential > PK-PD analysis. > > Consider a situation with substantial shrinkage in the estimated > individual PK (e.g. CL). > > When simulating PD based on dose from the model, it seems to me that > variability in PD will be over-predicted if the estimated variability in > CL (omega) is used. > > Would it then be appropriate to apply shrinkage to the variability in CL > prior to simulating the PD? > > Does this have any practical consequences, and is meaningful to consider? > > I have mostly seen examples of consequences of shrinkage in the context > of covariate analyses. Are there any examples relating to PK-PD > analyses. E.g simultaneous, versus sequential? > > Consider for example the situations below: > > I.e. analysis performed by: > > 1.Derive individual PK parameters. > > 2.Relate posthoc plasma conc to PD > > If doing the analysis based on dose, any variability in PK will show up > as variability in the dose-PD relationship. > > When doing the analysis based on plasma concentration (E.g. AUC), the > PK-variability is accounted for by the PK-model and will not influence > the variability in exposure response. > > However in the presence of shrinkage in the PK parameters, the situation > should be somewhere in-between these two scenarios and some of the > variability in PK will still show up as variability in PD. > > Hence when simulating based on the estimated variability in CL the > variability in pd should theoretically be exaggerated. > > Regards, > > Matts Kågedal > > Senior Pharmacometrician > > AstraZeneca > > ------------------------------------------------------------------------ > > *Confidentiality Notice: *This message is private and may contain > confidential and proprietary information. If you have received this > message in error, please notify us and remove it from your system and > note that you must not copy, distribute or take any action in reliance > on it. Any unauthorized use or disclosure of the contents of this > message is not permitted and may be unlawful.

On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote: > Hi Matts, > > One method to investigate the problem would be to conduct VPC. If VPC with model-estimated variances provides good (not inflated) range of PK profiles then one can argue that the PK model provides good description of the data and can be used for simulations (including PK-PD). > > Another test could be to do VPCs for the PK-PD model: one with fixed PK parameters (as was used in the sequential PK-PD modeling procedure) and the other one with model-simulated ETAs for both PK and PD parts. Again, if both provide good coverage of observed PK-PD data then combination of PK and PD models can be trusted, and any of the approaches can be applied. If one of the VPCs is inadequate, than it should be noticeable in the too narrow or too wide prediction intervals. > > Leonid On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote: > Resending, since my posting from this morning (below) has not yet appeared on nmusers. > > Apologies for any duplicate postings! > > *From:*Ribbing, Jakob > *Sent:* 18 February 2013 09:59 > *To:* "Kågedal, Matts"; [email protected] > *Cc:* Ribbing, Jakob > > *Subject:* RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis > > Hi Matts, > > I think you are correct; the problem you describe has not had much (public) discussion. > > It is also correct like you say that this is mostly a problem in case of all of the below > > ·sequential PK-PD analysis is applied (IPP approach, Zang et al) > > ·non-ignorable degree of shrinkage in PK parameters _of relevance_ > > oOf relevance: the PK parameters effectively driving PD for the mechanism, e.g. CL/F if AUC is driving. In addition, if PD response develops over several weeks/months then shrinkage in IOV may be ignored even for relevant PK parameters > > ·I would also like to add that for this to be an issue individual PK parameters must explain a fair degree of the variability in PD, which is not always the case > > oIf driving PD with typical PK parameters (along with dose and other PD covariates) does not increase PD omegas, compared to IPP, then either PK shrinkage is already massive, or else it is not an issue for the IPP-PD model > > If only the IPP approach is possible/practical a simplistic approach to simulate PD data is as follows: > > ·sample (with replacement) the individual PK parameters along with any potential covariates (maintaining correlation between IIP and covariates, i.e. whole subject vectors for these entities, but generally not for dose since generally should only have only random association with IPP or PK/PD-covariates) > > ·then use the re-sampled datasets for simulating PD according to the PD model (driven by IPP, covariates, dose, etc). The degree of shrinkage is then the same for PD estimation and simulation. > > This approach may for example allow to simulate realistic PD response at multiple dosing, based on only single dose PD. When the MD data becomes available then one may find that variabilities shift between PK and PD due to different PK shrinkage, but I would argue the simulated PD responses still were realistic. This approach is useful for predictions into the same population (especially if sufficient number of subjects available for re-sampling), but may not allow extrapolation into other populations where PK is projected to be different. > > When possible the obvious solution is to apply one of the alternative approaches to simultaneous PK-PD fit; after you have arrived at a final-IPP model. > > If a simultaneous fit is obtainable/practical this is the best option, but notice that e.g. if you have rich PK data in healthy and no PK data in patients (plus PD data in both populations): You can estimate separate omegas for PD parameters in healthy vs. patients, but it may be difficult to tell whether patients higher PD variability is due to PK shrinkage, or due to the actual PD variability being higher in this population (or both). PD variability may be confounded by a number of other factors that are actually variability in PK (fu, active metabolites and bio phase distribution, just to mention a few where information may be absent on the individual level). Depending on the purpose of the modelling this often not an issue, however. > > As you suggest there may be rare situations with IPP where a more complicated approach is needed, with a) simulation and re-estimation of PK model, to obtain Empirical-Bayes Estimates based on simulated data, and then feed these into the subsequent PD model. I would see this as a last resort. There are pitfalls in that if PD parameters have been estimated under one degree of PK shrinkage, then applying these estimates to a simulated example with different PK shrinkage requires adjustment of PD variability. I am not sure anyone has had to go down that route before and if not I hope you do not have to either. Maybe others can advice on this? > > Best regards > > Jakob > > Two methodological references: > > Simultaneous vs. sequential analysis for population PK/PD data II: robustness of methods. > > Zhang L, Beal SL, Sheiner LB. > > J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. > > Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance. > > Zhang L, Beal SL, Sheiner LB. > > J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404. -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53 email: [email protected] http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford

On 2/18/2013 3:23 PM, Nick Holford wrote: > Jakob, Leonid, > > I think it should be pointed out more clearly that PKPD analyses can > be performed most simply using the PPP&D method. This method has > better properties compared with IPP and is easier to set up and run > (see Zhang et al Part I). Sequential methods are preferred over > simultaneous methods whenever there is a possibility of > mis-specification of the model linking concentration to effect. This > is nearly always a real risk so the sequential method is rarely a > sensible choice (see Zhang et al part II). > > The IPP+SE method has properties similar to PPP&D but it technically > more challenging to use and cannot be used without a > variance-covariance matrix of the estimates. This can usually only be > obtained with oversimplified models (asymptotic $COV) or by time > consuming bootstraps. > > The bottom line is to use PPP&D. > > Leonid makes a good point about using VPC for model evaluation. The > VPC is capable of detecting structural model misspecification for > PKPD analyses but it is not foolproof. A misspecified random effects > model can compensate for a misspecified structural model. See > references below for an example. > > Best wishes, > > Nick > > Karlsson MO, Holford NHG 2008. A Tutorial on Visual Predictive > Checks. PAGE 17 (2008) Abstr 1434 [wwwpage-meetingorg/?abstract=1434] > (last accessed 11 February 2012). > > http://holford.fmhs.auckland.ac.nz/docs/vpc-tutorial-and-datatop.pdf > > On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote: > > > Hi Matts, > > > > One method to investigate the problem would be to conduct VPC. If > > VPC with model-estimated variances provides good (not inflated) > > range of PK profiles then one can argue that the PK model provides > > good description of the data and can be used for simulations > > (including PK-PD). > > > > Another test could be to do VPCs for the PK-PD model: one with > > fixed PK parameters (as was used in the sequential PK-PD modeling > > procedure) and the other one with model-simulated ETAs for both PK > > and PD parts. Again, if both provide good coverage of observed > > PK-PD data then combination of PK and PD models can be trusted, and > > any of the approaches can be applied. If one of the VPCs is > > inadequate, than it should be noticeable in the too narrow or too > > wide prediction intervals. > > > > Leonid > > On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote: > > > Resending, since my posting from this morning (below) has not yet > > appeared on nmusers. > > > > Apologies for any duplicate postings! > > > > *From:*Ribbing, Jakob *Sent:* 18 February 2013 09:59 *To:* > > "Kågedal, Matts"; [email protected] *Cc:* Ribbing, Jakob > > *Subject:* RE: Simulation settgin in the precence of Shrinkage in > > PK when doing PK-PD analysis > > > > Hi Matts, > > > > I think you are correct; the problem you describe has not had much > > (public) discussion. > > > > It is also correct like you say that this is mostly a problem in > > case of all of the below > > > > ·sequential PK-PD analysis is applied (IPP approach, Zang et al) > > > > ·non-ignorable degree of shrinkage in PK parameters _of relevance_ > > > > oOf relevance: the PK parameters effectively driving PD for the > > mechanism, e.g. CL/F if AUC is driving. In addition, if PD response > > develops over several weeks/months then shrinkage in IOV may be > > ignored even for relevant PK parameters > > > > ·I would also like to add that for this to be an issue individual > > PK parameters must explain a fair degree of the variability in PD, > > which is not always the case > > > > oIf driving PD with typical PK parameters (along with dose and > > other PD covariates) does not increase PD omegas, compared to IPP, > > then either PK shrinkage is already massive, or else it is not an > > issue for the IPP-PD model > > > > If only the IPP approach is possible/practical a simplistic > > approach to simulate PD data is as follows: > > > > ·sample (with replacement) the individual PK parameters along with > > any potential covariates (maintaining correlation between IIP and > > covariates, i.e. whole subject vectors for these entities, but > > generally not for dose since generally should only have only random > > association with IPP or PK/PD-covariates) > > > > ·then use the re-sampled datasets for simulating PD according to > > the PD model (driven by IPP, covariates, dose, etc). The degree of > > shrinkage is then the same for PD estimation and simulation. > > > > This approach may for example allow to simulate realistic PD > > response at multiple dosing, based on only single dose PD. When the > > MD data becomes available then one may find that variabilities > > shift between PK and PD due to different PK shrinkage, but I would > > argue the simulated PD responses still were realistic. This > > approach is useful for predictions into the same population > > (especially if sufficient number of subjects available for > > re-sampling), but may not allow extrapolation into other > > populations where PK is projected to be different. > > > > When possible the obvious solution is to apply one of the > > alternative approaches to simultaneous PK-PD fit; after you have > > arrived at a final-IPP model. > > > > If a simultaneous fit is obtainable/practical this is the best > > option, but notice that e.g. if you have rich PK data in healthy > > and no PK data in patients (plus PD data in both populations): You > > can estimate separate omegas for PD parameters in healthy vs. > > patients, but it may be difficult to tell whether patients higher > > PD variability is due to PK shrinkage, or due to the actual PD > > variability being higher in this population (or both). PD > > variability may be confounded by a number of other factors that are > > actually variability in PK (fu, active metabolites and bio phase > > distribution, just to mention a few where information may be absent > > on the individual level). Depending on the purpose of the modelling > > this often not an issue, however. > > > > As you suggest there may be rare situations with IPP where a more > > complicated approach is needed, with a) simulation and > > re-estimation of PK model, to obtain Empirical-Bayes Estimates > > based on simulated data, and then feed these into the subsequent PD > > model. I would see this as a last resort. There are pitfalls in > > that if PD parameters have been estimated under one degree of PK > > shrinkage, then applying these estimates to a simulated example > > with different PK shrinkage requires adjustment of PD variability. > > I am not sure anyone has had to go down that route before and if > > not I hope you do not have to either. Maybe others can advice on > > this? > > > > Best regards > > > > Jakob > > > > Two methodological references: > > > > Simultaneous vs. sequential analysis for population PK/PD data II: > > robustness of methods. > > > > Zhang L, Beal SL, Sheiner LB. > > > > J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. > > > > Simultaneous vs. sequential analysis for population PK/PD data I: > > best-case performance. > > > > Zhang L, Beal SL, Sheiner LB. > > > > J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404. e

On 19/02/2013 1:57 p.m., Leonid Gibiansky wrote: > Hi Nick, > I am afraid, many users are not very familiar with all the important > abbreviations - PPP&D, IPP, VINA, etc. :) > > I am not sure that I follow your points, looks contradictory: > > > Sequential methods are preferred .... > > ...sequential method is rarely a sensible choice > > In any case, I would not agree that PPP&D (use of both PK and PD data in > the simultaneous fit ?) is the only good method (or even that this > method is the best in all situations). In many cases, sequential method > gives nearly identical results to the simultaneous fit, and was easier > to implement numerically. I would be curious to see a good real life > example where sequential method provided the wrong answer while PP&D was > > correct (that is, the example where these two methods resulted in different clinically relevant conclusions). > > Leonid > > VINA = Very Important New Abbreviation > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > On 2/18/2013 3:23 PM, Nick Holford wrote: > > > Jakob, Leonid, > > > > I think it should be pointed out more clearly that PKPD analyses can > > be performed most simply using the PPP&D method. This method has > > better properties compared with IPP and is easier to set up and run > > (see Zhang et al Part I). Sequential methods are preferred over > > simultaneous methods whenever there is a possibility of > > mis-specification of the model linking concentration to effect. This > > is nearly always a real risk so the sequential method is rarely a > > sensible choice (see Zhang et al part II). > > > > The IPP+SE method has properties similar to PPP&D but it technically > > more challenging to use and cannot be used without a > > variance-covariance matrix of the estimates. This can usually only be > > obtained with oversimplified models (asymptotic $COV) or by time > > consuming bootstraps. > > > > The bottom line is to use PPP&D. > > > > Leonid makes a good point about using VPC for model evaluation. The > > VPC is capable of detecting structural model misspecification for > > PKPD analyses but it is not foolproof. A misspecified random effects > > model can compensate for a misspecified structural model. See > > references below for an example. > > > > Best wishes, > > > > Nick > > > > Karlsson MO, Holford NHG 2008. A Tutorial on Visual Predictive > > Checks. PAGE 17 (2008) Abstr 1434 [wwwpage-meetingorg/?abstract=1434] > > (last accessed 11 February 2012). > > > > http://holford.fmhs.auckland.ac.nz/docs/vpc-tutorial-and-datatop.pdf > > > > On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote: > > > > > Hi Matts, > > > > > > One method to investigate the problem would be to conduct VPC. If > > > VPC with model-estimated variances provides good (not inflated) > > > range of PK profiles then one can argue that the PK model provides > > > good description of the data and can be used for simulations > > > (including PK-PD). > > > > > > Another test could be to do VPCs for the PK-PD model: one with > > > fixed PK parameters (as was used in the sequential PK-PD modeling > > > procedure) and the other one with model-simulated ETAs for both PK > > > and PD parts. Again, if both provide good coverage of observed > > > PK-PD data then combination of PK and PD models can be trusted, and > > > any of the approaches can be applied. If one of the VPCs is > > > inadequate, than it should be noticeable in the too narrow or too > > > wide prediction intervals. > > > > > > Leonid > > > > On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote: > > > > > Resending, since my posting from this morning (below) has not yet > > > appeared on nmusers. > > > > > > Apologies for any duplicate postings! > > > > > > *From:*Ribbing, Jakob *Sent:* 18 February 2013 09:59 *To:* > > > "Kågedal, Matts"; [email protected] *Cc:* Ribbing, Jakob > > > *Subject:* RE: Simulation settgin in the precence of Shrinkage in > > > PK when doing PK-PD analysis > > > > > > Hi Matts, > > > > > > I think you are correct; the problem you describe has not had much > > > (public) discussion. > > > > > > It is also correct like you say that this is mostly a problem in > > > case of all of the below > > > > > > ·sequential PK-PD analysis is applied (IPP approach, Zang et al) > > > > > > ·non-ignorable degree of shrinkage in PK parameters _of relevance_ > > > > > > oOf relevance: the PK parameters effectively driving PD for the > > > mechanism, e.g. CL/F if AUC is driving. In addition, if PD response > > > develops over several weeks/months then shrinkage in IOV may be > > > ignored even for relevant PK parameters > > > > > > ·I would also like to add that for this to be an issue individual > > > PK parameters must explain a fair degree of the variability in PD, > > > which is not always the case > > > > > > oIf driving PD with typical PK parameters (along with dose and > > > other PD covariates) does not increase PD omegas, compared to IPP, > > > then either PK shrinkage is already massive, or else it is not an > > > issue for the IPP-PD model > > > > > > If only the IPP approach is possible/practical a simplistic > > > approach to simulate PD data is as follows: > > > > > > ·sample (with replacement) the individual PK parameters along with > > > any potential covariates (maintaining correlation between IIP and > > > covariates, i.e. whole subject vectors for these entities, but > > > generally not for dose since generally should only have only random > > > association with IPP or PK/PD-covariates) > > > > > > ·then use the re-sampled datasets for simulating PD according to > > > the PD model (driven by IPP, covariates, dose, etc). The degree of > > > shrinkage is then the same for PD estimation and simulation. > > > > > > This approach may for example allow to simulate realistic PD > > > response at multiple dosing, based on only single dose PD. When the > > > MD data becomes available then one may find that variabilities > > > shift between PK and PD due to different PK shrinkage, but I would > > > argue the simulated PD responses still were realistic. This > > > approach is useful for predictions into the same population > > > (especially if sufficient number of subjects available for > > > re-sampling), but may not allow extrapolation into other > > > populations where PK is projected to be different. > > > > > > When possible the obvious solution is to apply one of the > > > alternative approaches to simultaneous PK-PD fit; after you have > > > arrived at a final-IPP model. > > > > > > If a simultaneous fit is obtainable/practical this is the best > > > option, but notice that e.g. if you have rich PK data in healthy > > > and no PK data in patients (plus PD data in both populations): You > > > can estimate separate omegas for PD parameters in healthy vs. > > > patients, but it may be difficult to tell whether patients higher > > > PD variability is due to PK shrinkage, or due to the actual PD > > > variability being higher in this population (or both). PD > > > variability may be confounded by a number of other factors that are > > > actually variability in PK (fu, active metabolites and bio phase > > > distribution, just to mention a few where information may be absent > > > on the individual level). Depending on the purpose of the modelling > > > this often not an issue, however. > > > > > > As you suggest there may be rare situations with IPP where a more > > > complicated approach is needed, with a) simulation and > > > re-estimation of PK model, to obtain Empirical-Bayes Estimates > > > based on simulated data, and then feed these into the subsequent PD > > > model. I would see this as a last resort. There are pitfalls in > > > that if PD parameters have been estimated under one degree of PK > > > shrinkage, then applying these estimates to a simulated example > > > with different PK shrinkage requires adjustment of PD variability. > > > I am not sure anyone has had to go down that route before and if > > > not I hope you do not have to either. Maybe others can advice on > > > this? > > > > > > Best regards > > > > > > Jakob > > > > > > Two methodological references: > > > > > > Simultaneous vs. sequential analysis for population PK/PD data II: > > > robustness of methods. > > > > > > Zhang L, Beal SL, Sheiner LB. > > > > > > J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. > > > > > > Simultaneous vs. sequential analysis for population PK/PD data I: > > > best-case performance. > > > > > > Zhang L, Beal SL, Sheiner LB. > > > > > > J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404. > > e -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53 email: [email protected] http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford

-----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Nick Holford Sent: Tuesday, February 19, 2013 2:16 To: nmusers Subject: Re: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Leonid, Thanks for pointing out the confusion in my response. I intended to write: "Sequential methods are preferred over simultaneous methods whenever there is a possibility of mis-specification of the model linking concentration to effect. This is nearly always a real risk so the simultaneous method is rarely a sensible choice (see Zhang et al part II). " The VIOA (very important old abbreviations http://www.cognigencorp.com/nonmem/nm/99dec192005.html) IPP, PPP&D are defined in the Zhang papers. Anybody who wants to do serious PKPD work should be familiar with these papers. The simulations performed by Zhang et al. show that simultaneous can be worse than sequential (see Part II paper). That is why I encourage a sequential approach using the PPP&D method. Best wishes, Nick On 19/02/2013 1:57 p.m., Leonid Gibiansky wrote: > Hi Nick, > I am afraid, many users are not very familiar with all the important > abbreviations - PPP&D, IPP, VINA, etc. :) > > I am not sure that I follow your points, looks contradictory: > > > Sequential methods are preferred .... > > ...sequential method is rarely a sensible choice > > In any case, I would not agree that PPP&D (use of both PK and PD data in > the simultaneous fit ?) is the only good method (or even that this > method is the best in all situations). In many cases, sequential method > gives nearly identical results to the simultaneous fit, and was easier > to implement numerically. I would be curious to see a good real life > example where sequential method provided the wrong answer while PP&D was > correct (that is, the example where these two methods resulted in > different clinically relevant conclusions). > > Leonid > > VINA = Very Important New Abbreviation > > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > > > On 2/18/2013 3:23 PM, Nick Holford wrote: >> Jakob, Leonid, >> >> I think it should be pointed out more clearly that PKPD analyses can >> be performed most simply using the PPP&D method. This method has >> better properties compared with IPP and is easier to set up and run >> (see Zhang et al Part I). Sequential methods are preferred over >> simultaneous methods whenever there is a possibility of >> mis-specification of the model linking concentration to effect. This >> is nearly always a real risk so the sequential method is rarely a >> sensible choice (see Zhang et al part II). >> >> The IPP+SE method has properties similar to PPP&D but it technically >> more challenging to use and cannot be used without a >> variance-covariance matrix of the estimates. This can usually only be >> obtained with oversimplified models (asymptotic $COV) or by time >> consuming bootstraps. >> >> The bottom line is to use PPP&D. >> >> Leonid makes a good point about using VPC for model evaluation. The >> VPC is capable of detecting structural model misspecification for >> PKPD analyses but it is not foolproof. A misspecified random effects >> model can compensate for a misspecified structural model. See >> references below for an example. >> >> Best wishes, >> >> Nick >> >> Karlsson MO, Holford NHG 2008. A Tutorial on Visual Predictive >> Checks. PAGE 17 (2008) Abstr 1434 [wwwpage-meetingorg/?abstract=1434] >> (last accessed 11 February 2012). >> >> http://holford.fmhs.auckland.ac.nz/docs/vpc-tutorial-and-datatop.pdf >> >> >> On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote: >>> Hi Matts, >>> >>> One method to investigate the problem would be to conduct VPC. If >>> VPC with model-estimated variances provides good (not inflated) >>> range of PK profiles then one can argue that the PK model provides >>> good description of the data and can be used for simulations >>> (including PK-PD). >>> >>> Another test could be to do VPCs for the PK-PD model: one with >>> fixed PK parameters (as was used in the sequential PK-PD modeling >>> procedure) and the other one with model-simulated ETAs for both PK >>> and PD parts. Again, if both provide good coverage of observed >>> PK-PD data then combination of PK and PD models can be trusted, and >>> any of the approaches can be applied. If one of the VPCs is >>> inadequate, than it should be noticeable in the too narrow or too >>> wide prediction intervals. >>> >>> Leonid >> >> On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote: >>> >>> Resending, since my posting from this morning (below) has not yet >>> appeared on nmusers. >>> >>> Apologies for any duplicate postings! >>> >>> *From:*Ribbing, Jakob *Sent:* 18 February 2013 09:59 *To:* >>> "Kågedal, Matts"; [email protected] *Cc:* Ribbing, Jakob >>> *Subject:* RE: Simulation settgin in the precence of Shrinkage in >>> PK when doing PK-PD analysis >>> >>> Hi Matts, >>> >>> I think you are correct; the problem you describe has not had much >>> (public) discussion. >>> >>> It is also correct like you say that this is mostly a problem in >>> case of all of the below >>> >>> ·sequential PK-PD analysis is applied (IPP approach, Zang et al) >>> >>> ·non-ignorable degree of shrinkage in PK parameters _of relevance_ >>> >>> oOf relevance: the PK parameters effectively driving PD for the >>> mechanism, e.g. CL/F if AUC is driving. In addition, if PD response >>> develops over several weeks/months then shrinkage in IOV may be >>> ignored even for relevant PK parameters >>> >>> ·I would also like to add that for this to be an issue individual >>> PK parameters must explain a fair degree of the variability in PD, >>> which is not always the case >>> >>> oIf driving PD with typical PK parameters (along with dose and >>> other PD covariates) does not increase PD omegas, compared to IPP, >>> then either PK shrinkage is already massive, or else it is not an >>> issue for the IPP-PD model >>> >>> If only the IPP approach is possible/practical a simplistic >>> approach to simulate PD data is as follows: >>> >>> ·sample (with replacement) the individual PK parameters along with >>> any potential covariates (maintaining correlation between IIP and >>> covariates, i.e. whole subject vectors for these entities, but >>> generally not for dose since generally should only have only random >>> association with IPP or PK/PD-covariates) >>> >>> ·then use the re-sampled datasets for simulating PD according to >>> the PD model (driven by IPP, covariates, dose, etc). The degree of >>> shrinkage is then the same for PD estimation and simulation. >>> >>> This approach may for example allow to simulate realistic PD >>> response at multiple dosing, based on only single dose PD. When the >>> MD data becomes available then one may find that variabilities >>> shift between PK and PD due to different PK shrinkage, but I would >>> argue the simulated PD responses still were realistic. This >>> approach is useful for predictions into the same population >>> (especially if sufficient number of subjects available for >>> re-sampling), but may not allow extrapolation into other >>> populations where PK is projected to be different. >>> >>> When possible the obvious solution is to apply one of the >>> alternative approaches to simultaneous PK-PD fit; after you have >>> arrived at a final-IPP model. >>> >>> If a simultaneous fit is obtainable/practical this is the best >>> option, but notice that e.g. if you have rich PK data in healthy >>> and no PK data in patients (plus PD data in both populations): You >>> can estimate separate omegas for PD parameters in healthy vs. >>> patients, but it may be difficult to tell whether patients higher >>> PD variability is due to PK shrinkage, or due to the actual PD >>> variability being higher in this population (or both). PD >>> variability may be confounded by a number of other factors that are >>> actually variability in PK (fu, active metabolites and bio phase >>> distribution, just to mention a few where information may be absent >>> on the individual level). Depending on the purpose of the modelling >>> this often not an issue, however. >>> >>> As you suggest there may be rare situations with IPP where a more >>> complicated approach is needed, with a) simulation and >>> re-estimation of PK model, to obtain Empirical-Bayes Estimates >>> based on simulated data, and then feed these into the subsequent PD >>> model. I would see this as a last resort. There are pitfalls in >>> that if PD parameters have been estimated under one degree of PK >>> shrinkage, then applying these estimates to a simulated example >>> with different PK shrinkage requires adjustment of PD variability. >>> I am not sure anyone has had to go down that route before and if >>> not I hope you do not have to either. Maybe others can advice on >>> this? >>> >>> Best regards >>> >>> Jakob >>> >>> Two methodological references: >>> >>> Simultaneous vs. sequential analysis for population PK/PD data II: >>> robustness of methods. >>> >>> Zhang L, Beal SL, Sheiner LB. >>> >>> J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. >>> >>> Simultaneous vs. sequential analysis for population PK/PD data I: >>> best-case performance. >>> >>> Zhang L, Beal SL, Sheiner LB. >>> >>> J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404. >>> >> > e -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53 email: [email protected] http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford Notice: This e-mail message, together with any attachments, contains information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA 08889), and/or its affiliates Direct contact information for affiliates is available at http://www.merck.com/contact/contacts.html) that may be confidential, proprietary copyrighted and/or legally privileged. It is intended solely for the use of the individual or entity named on this message. If you are not the intended recipient, and have received this message in error, please notify us immediately by reply e-mail and then delete it from your system.

-----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Elassaiss - Schaap, J (Jeroen) Sent: Wednesday, February 20, 2013 8:38 AM To: Nick Holford; nmusers Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi, Let me clarify/call out one aspect that may not be obvious to all, it is underlying all the excellent replies (not to say the new abbreviations ;-). Shrinkage is only a problem in sequential analysis but not in simultaneous analysis. In sequential analysis one relies on posthoc estimates that are impacted by shrinkage; the simultaneous approach used variance estimates (omegas) which are not impacted. And, as others pointed out, PK shrinkage may not be the first aspect one needs to worry about in developing a PK-PD model but it is very useful to consider its potential impact in the more final stages of model development. Simultaneous analysis also has the benefit of accounting for correlation between PK and PD parameters and therefore provides a better handle for simulations in uncertainty. Best regards, Jeroen J. Elassaiss-Schaap Senior Principal Scientist Phone: + 31 412 66 9320 MSD | PK, PD and Drug Metabolism | Clinical PK-PD Mail stop KR 4406 | PO Box 20, 5340 BH Oss, NL -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Nick Holford Sent: Tuesday, February 19, 2013 2:16 To: nmusers Subject: Re: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Leonid, Thanks for pointing out the confusion in my response. I intended to write: "Sequential methods are preferred over simultaneous methods whenever there is a possibility of mis-specification of the model linking concentration to effect. This is nearly always a real risk so the simultaneous method is rarely a sensible choice (see Zhang et al part II). " The VIOA (very important old abbreviations http://www.cognigencorp.com/nonmem/nm/99dec192005.html) IPP, PPP&D are defined in the Zhang papers. Anybody who wants to do serious PKPD work should be familiar with these papers. The simulations performed by Zhang et al. show that simultaneous can be worse than sequential (see Part II paper). That is why I encourage a sequential approach using the PPP&D method. Best wishes, Nick On 19/02/2013 1:57 p.m., Leonid Gibiansky wrote: > Hi Nick, > I am afraid, many users are not very familiar with all the important > abbreviations - PPP&D, IPP, VINA, etc. :) > > I am not sure that I follow your points, looks contradictory: > > > Sequential methods are preferred .... > > ...sequential method is rarely a sensible choice > > In any case, I would not agree that PPP&D (use of both PK and PD data > in the simultaneous fit ?) is the only good method (or even that this > method is the best in all situations). In many cases, sequential > method gives nearly identical results to the simultaneous fit, and was > easier to implement numerically. I would be curious to see a good real > life example where sequential method provided the wrong answer while > PP&D was correct (that is, the example where these two methods > resulted in different clinically relevant conclusions). > > Leonid > > VINA = Very Important New Abbreviation > > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > > > On 2/18/2013 3:23 PM, Nick Holford wrote: >> Jakob, Leonid, >> >> I think it should be pointed out more clearly that PKPD analyses can >> be performed most simply using the PPP&D method. This method has >> better properties compared with IPP and is easier to set up and run >> (see Zhang et al Part I). Sequential methods are preferred over >> simultaneous methods whenever there is a possibility of >> mis-specification of the model linking concentration to effect. This >> is nearly always a real risk so the sequential method is rarely a >> sensible choice (see Zhang et al part II). >> >> The IPP+SE method has properties similar to PPP&D but it technically >> more challenging to use and cannot be used without a >> variance-covariance matrix of the estimates. This can usually only be >> obtained with oversimplified models (asymptotic $COV) or by time >> consuming bootstraps. >> >> The bottom line is to use PPP&D. >> >> Leonid makes a good point about using VPC for model evaluation. The >> VPC is capable of detecting structural model misspecification for >> PKPD analyses but it is not foolproof. A misspecified random effects >> model can compensate for a misspecified structural model. See >> references below for an example. >> >> Best wishes, >> >> Nick >> >> Karlsson MO, Holford NHG 2008. A Tutorial on Visual Predictive >> Checks. PAGE 17 (2008) Abstr 1434 [wwwpage-meetingorg/?abstract=1434] >> (last accessed 11 February 2012). >> >> http://holford.fmhs.auckland.ac.nz/docs/vpc-tutorial-and-datatop.pdf >> >> >> On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote: >>> Hi Matts, >>> >>> One method to investigate the problem would be to conduct VPC. If >>> VPC with model-estimated variances provides good (not inflated) >>> range of PK profiles then one can argue that the PK model provides >>> good description of the data and can be used for simulations >>> (including PK-PD). >>> >>> Another test could be to do VPCs for the PK-PD model: one with fixed >>> PK parameters (as was used in the sequential PK-PD modeling >>> procedure) and the other one with model-simulated ETAs for both PK >>> and PD parts. Again, if both provide good coverage of observed PK-PD >>> data then combination of PK and PD models can be trusted, and any of >>> the approaches can be applied. If one of the VPCs is inadequate, >>> than it should be noticeable in the too narrow or too wide >>> prediction intervals. >>> >>> Leonid >> >> On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote: >>> >>> Resending, since my posting from this morning (below) has not yet >>> appeared on nmusers. >>> >>> Apologies for any duplicate postings! >>> >>> *From:*Ribbing, Jakob *Sent:* 18 February 2013 09:59 *To:* "Kågedal, >>> Matts"; [email protected] *Cc:* Ribbing, Jakob >>> *Subject:* RE: Simulation settgin in the precence of Shrinkage in PK >>> when doing PK-PD analysis >>> >>> Hi Matts, >>> >>> I think you are correct; the problem you describe has not had much >>> (public) discussion. >>> >>> It is also correct like you say that this is mostly a problem in >>> case of all of the below >>> >>> ·sequential PK-PD analysis is applied (IPP approach, Zang et al) >>> >>> ·non-ignorable degree of shrinkage in PK parameters _of relevance_ >>> >>> oOf relevance: the PK parameters effectively driving PD for the >>> mechanism, e.g. CL/F if AUC is driving. In addition, if PD response >>> develops over several weeks/months then shrinkage in IOV may be >>> ignored even for relevant PK parameters >>> >>> ·I would also like to add that for this to be an issue individual PK >>> parameters must explain a fair degree of the variability in PD, >>> which is not always the case >>> >>> oIf driving PD with typical PK parameters (along with dose and other >>> PD covariates) does not increase PD omegas, compared to IPP, then >>> either PK shrinkage is already massive, or else it is not an issue >>> for the IPP-PD model >>> >>> If only the IPP approach is possible/practical a simplistic approach >>> to simulate PD data is as follows: >>> >>> ·sample (with replacement) the individual PK parameters along with >>> any potential covariates (maintaining correlation between IIP and >>> covariates, i.e. whole subject vectors for these entities, but >>> generally not for dose since generally should only have only random >>> association with IPP or PK/PD-covariates) >>> >>> ·then use the re-sampled datasets for simulating PD according to the >>> PD model (driven by IPP, covariates, dose, etc). The degree of >>> shrinkage is then the same for PD estimation and simulation. >>> >>> This approach may for example allow to simulate realistic PD >>> response at multiple dosing, based on only single dose PD. When the >>> MD data becomes available then one may find that variabilities shift >>> between PK and PD due to different PK shrinkage, but I would argue >>> the simulated PD responses still were realistic. This approach is >>> useful for predictions into the same population (especially if >>> sufficient number of subjects available for re-sampling), but may >>> not allow extrapolation into other populations where PK is projected >>> to be different. >>> >>> When possible the obvious solution is to apply one of the >>> alternative approaches to simultaneous PK-PD fit; after you have >>> arrived at a final-IPP model. >>> >>> If a simultaneous fit is obtainable/practical this is the best >>> option, but notice that e.g. if you have rich PK data in healthy and >>> no PK data in patients (plus PD data in both populations): You can >>> estimate separate omegas for PD parameters in healthy vs. >>> patients, but it may be difficult to tell whether patients higher PD >>> variability is due to PK shrinkage, or due to the actual PD >>> variability being higher in this population (or both). PD >>> variability may be confounded by a number of other factors that are >>> actually variability in PK (fu, active metabolites and bio phase >>> distribution, just to mention a few where information may be absent >>> on the individual level). Depending on the purpose of the modelling >>> this often not an issue, however. >>> >>> As you suggest there may be rare situations with IPP where a more >>> complicated approach is needed, with a) simulation and re-estimation >>> of PK model, to obtain Empirical-Bayes Estimates based on simulated >>> data, and then feed these into the subsequent PD model. I would see >>> this as a last resort. There are pitfalls in that if PD parameters >>> have been estimated under one degree of PK shrinkage, then applying >>> these estimates to a simulated example with different PK shrinkage >>> requires adjustment of PD variability. >>> I am not sure anyone has had to go down that route before and if not >>> I hope you do not have to either. Maybe others can advice on this? >>> >>> Best regards >>> >>> Jakob >>> >>> Two methodological references: >>> >>> Simultaneous vs. sequential analysis for population PK/PD data II: >>> robustness of methods. >>> >>> Zhang L, Beal SL, Sheiner LB. >>> >>> J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. >>> >>> Simultaneous vs. sequential analysis for population PK/PD data I: >>> best-case performance. >>> >>> Zhang L, Beal SL, Sheiner LB. >>> >>> J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404. >>> >> > e -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53 email: [email protected] http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford Notice: This e-mail message, together with any attachments, contains information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA 08889), and/or its affiliates Direct contact information for affiliates is available at http://www.merck.com/contact/contacts.html) that may be confidential, proprietary copyrighted and/or legally privileged. It is intended solely for the use of the individual or entity named on this message. If you are not the intended recipient, and have received this message in error, please notify us immediately by reply e-mail and then delete it from your system.

-----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Ken Kowalski Sent: miércoles, 20 de febrero de 2013 9:1 To: 'Elassaiss - Schaap, J (Jeroen)'; 'Nick Holford'; 'nmusers' Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi Jeroen, I believe the feature that you describe for a simultaneous fit also applies to the PPP&D sequential approach that Nick advocates (which I also like). The framework of the PPP&D approach is to set it up the same as you would a simultaneous model fit but you fix the PK parameters (PK elements of theta, omega and sigma) to the final estimates from an independent fit to the PK data alone. It is a sequential approach that does not use the posthoc estimates of the PK parameters directly in the specification of the model as does the IPP sequential approach. The PPP&D approach by its sequential nature does not account for the correlation between the PK and PD parameter estimates. This can be a drawback or a feature of the approach depending on the level of model misspecification. When there is substantial PD model misspecification, a simultaneous model fit can lead to biased estimates of the PK parameters. The PPP&D approach guards against PD model misspecification impacting the PK parameter estimates since they are held fixed based on a separate (independent) fit to the PK data alone. Best, Ken Kenneth G. Kowalski President & CEO A2PG - Ann Arbor Pharmacometrics Group, Inc. 110 Miller Ave., Garden Suite Ann Arbor, MI 48104 Work: 734-274-8255 Cell: 248-207-5082 Fax: 734-913-0230 [email protected] www.a2pg.com -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Elassaiss - Schaap, J (Jeroen) Sent: Wednesday, February 20, 2013 8:38 AM To: Nick Holford; nmusers Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi, Let me clarify/call out one aspect that may not be obvious to all, it is underlying all the excellent replies (not to say the new abbreviations ;-). Shrinkage is only a problem in sequential analysis but not in simultaneous analysis. In sequential analysis one relies on posthoc estimates that are impacted by shrinkage; the simultaneous approach used variance estimates (omegas) which are not impacted. And, as others pointed out, PK shrinkage may not be the first aspect one needs to worry about in developing a PK-PD model but it is very useful to consider its potential impact in the more final stages of model development. Simultaneous analysis also has the benefit of accounting for correlation between PK and PD parameters and therefore provides a better handle for simulations in uncertainty. Best regards, Jeroen J. Elassaiss-Schaap Senior Principal Scientist Phone: + 31 412 66 9320 MSD | PK, PD and Drug Metabolism | Clinical PK-PD Mail stop KR 4406 | PO Box 20, 5340 BH Oss, NL -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Nick Holford Sent: Tuesday, February 19, 2013 2:16 To: nmusers Subject: Re: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Leonid, Thanks for pointing out the confusion in my response. I intended to write: "Sequential methods are preferred over simultaneous methods whenever there is a possibility of mis-specification of the model linking concentration to effect. This is nearly always a real risk so the simultaneous method is rarely a sensible choice (see Zhang et al part II). " The VIOA (very important old abbreviations http://www.cognigencorp.com/nonmem/nm/99dec192005.html) IPP, PPP&D are defined in the Zhang papers. Anybody who wants to do serious PKPD work should be familiar with these papers. The simulations performed by Zhang et al. show that simultaneous can be worse than sequential (see Part II paper). That is why I encourage a sequential approach using the PPP&D method. Best wishes, Nick On 19/02/2013 1:57 p.m., Leonid Gibiansky wrote: > Hi Nick, > I am afraid, many users are not very familiar with all the important > abbreviations - PPP&D, IPP, VINA, etc. :) > > I am not sure that I follow your points, looks contradictory: > > > Sequential methods are preferred .... > > ...sequential method is rarely a sensible choice > > In any case, I would not agree that PPP&D (use of both PK and PD data > in the simultaneous fit ?) is the only good method (or even that this > method is the best in all situations). In many cases, sequential > method gives nearly identical results to the simultaneous fit, and was > easier to implement numerically. I would be curious to see a good real > life example where sequential method provided the wrong answer while > PP&D was correct (that is, the example where these two methods > resulted in different clinically relevant conclusions). > > Leonid > > VINA = Very Important New Abbreviation > > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > > > On 2/18/2013 3:23 PM, Nick Holford wrote: >> Jakob, Leonid, >> >> I think it should be pointed out more clearly that PKPD analyses can >> be performed most simply using the PPP&D method. This method has >> better properties compared with IPP and is easier to set up and run >> (see Zhang et al Part I). Sequential methods are preferred over >> simultaneous methods whenever there is a possibility of >> mis-specification of the model linking concentration to effect. This >> is nearly always a real risk so the sequential method is rarely a >> sensible choice (see Zhang et al part II). >> >> The IPP+SE method has properties similar to PPP&D but it technically >> more challenging to use and cannot be used without a >> variance-covariance matrix of the estimates. This can usually only be >> obtained with oversimplified models (asymptotic $COV) or by time >> consuming bootstraps. >> >> The bottom line is to use PPP&D. >> >> Leonid makes a good point about using VPC for model evaluation. The >> VPC is capable of detecting structural model misspecification for >> PKPD analyses but it is not foolproof. A misspecified random effects >> model can compensate for a misspecified structural model. See >> references below for an example. >> >> Best wishes, >> >> Nick >> >> Karlsson MO, Holford NHG 2008. A Tutorial on Visual Predictive >> Checks. PAGE 17 (2008) Abstr 1434 [wwwpage-meetingorg/?abstract=1434] >> (last accessed 11 February 2012). >> >> http://holford.fmhs.auckland.ac.nz/docs/vpc-tutorial-and-datatop.pdf >> >> >> On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote: >>> Hi Matts, >>> >>> One method to investigate the problem would be to conduct VPC. If >>> VPC with model-estimated variances provides good (not inflated) >>> range of PK profiles then one can argue that the PK model provides >>> good description of the data and can be used for simulations >>> (including PK-PD). >>> >>> Another test could be to do VPCs for the PK-PD model: one with fixed >>> PK parameters (as was used in the sequential PK-PD modeling >>> procedure) and the other one with model-simulated ETAs for both PK >>> and PD parts. Again, if both provide good coverage of observed PK-PD >>> data then combination of PK and PD models can be trusted, and any of >>> the approaches can be applied. If one of the VPCs is inadequate, >>> than it should be noticeable in the too narrow or too wide >>> prediction intervals. >>> >>> Leonid >> >> On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote: >>> >>> Resending, since my posting from this morning (below) has not yet >>> appeared on nmusers. >>> >>> Apologies for any duplicate postings! >>> >>> *From:*Ribbing, Jakob *Sent:* 18 February 2013 09:59 *To:* "Kågedal, >>> Matts"; [email protected] *Cc:* Ribbing, Jakob >>> *Subject:* RE: Simulation settgin in the precence of Shrinkage in PK >>> when doing PK-PD analysis >>> >>> Hi Matts, >>> >>> I think you are correct; the problem you describe has not had much >>> (public) discussion. >>> >>> It is also correct like you say that this is mostly a problem in >>> case of all of the below >>> >>> ·sequential PK-PD analysis is applied (IPP approach, Zang et al) >>> >>> ·non-ignorable degree of shrinkage in PK parameters _of relevance_ >>> >>> oOf relevance: the PK parameters effectively driving PD for the >>> mechanism, e.g. CL/F if AUC is driving. In addition, if PD response >>> develops over several weeks/months then shrinkage in IOV may be >>> ignored even for relevant PK parameters >>> >>> ·I would also like to add that for this to be an issue individual PK >>> parameters must explain a fair degree of the variability in PD, >>> which is not always the case >>> >>> oIf driving PD with typical PK parameters (along with dose and other >>> PD covariates) does not increase PD omegas, compared to IPP, then >>> either PK shrinkage is already massive, or else it is not an issue >>> for the IPP-PD model >>> >>> If only the IPP approach is possible/practical a simplistic approach >>> to simulate PD data is as follows: >>> >>> ·sample (with replacement) the individual PK parameters along with >>> any potential covariates (maintaining correlation between IIP and >>> covariates, i.e. whole subject vectors for these entities, but >>> generally not for dose since generally should only have only random >>> association with IPP or PK/PD-covariates) >>> >>> ·then use the re-sampled datasets for simulating PD according to the >>> PD model (driven by IPP, covariates, dose, etc). The degree of >>> shrinkage is then the same for PD estimation and simulation. >>> >>> This approach may for example allow to simulate realistic PD >>> response at multiple dosing, based on only single dose PD. When the >>> MD data becomes available then one may find that variabilities shift >>> between PK and PD due to different PK shrinkage, but I would argue >>> the simulated PD responses still were realistic. This approach is >>> useful for predictions into the same population (especially if >>> sufficient number of subjects available for re-sampling), but may >>> not allow extrapolation into other populations where PK is projected >>> to be different. >>> >>> When possible the obvious solution is to apply one of the >>> alternative approaches to simultaneous PK-PD fit; after you have >>> arrived at a final-IPP model. >>> >>> If a simultaneous fit is obtainable/practical this is the best >>> option, but notice that e.g. if you have rich PK data in healthy and >>> no PK data in patients (plus PD data in both populations): You can >>> estimate separate omegas for PD parameters in healthy vs. >>> patients, but it may be difficult to tell whether patients higher PD >>> variability is due to PK shrinkage, or due to the actual PD >>> variability being higher in this population (or both). PD >>> variability may be confounded by a number of other factors that are >>> actually variability in PK (fu, active metabolites and bio phase >>> distribution, just to mention a few where information may be absent >>> on the individual level). Depending on the purpose of the modelling >>> this often not an issue, however. >>> >>> As you suggest there may be rare situations with IPP where a more >>> complicated approach is needed, with a) simulation and re-estimation >>> of PK model, to obtain Empirical-Bayes Estimates based on simulated >>> data, and then feed these into the subsequent PD model. I would see >>> this as a last resort. There are pitfalls in that if PD parameters >>> have been estimated under one degree of PK shrinkage, then applying >>> these estimates to a simulated example with different PK shrinkage >>> requires adjustment of PD variability. >>> I am not sure anyone has had to go down that route before and if not >>> I hope you do not have to either. Maybe others can advice on this? >>> >>> Best regards >>> >>> Jakob >>> >>> Two methodological references: >>> >>> Simultaneous vs. sequential analysis for population PK/PD data II: >>> robustness of methods. >>> >>> Zhang L, Beal SL, Sheiner LB. >>> >>> J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. >>> >>> Simultaneous vs. sequential analysis for population PK/PD data I: >>> best-case performance. >>> >>> Zhang L, Beal SL, Sheiner LB. >>> >>> J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404. >>> >> > e -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53 email: [email protected] http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford Notice: This e-mail message, together with any attachments, contains information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA 08889), and/or its affiliates Direct contact information for affiliates is available at http://www.merck.com/contact/contacts.html) that may be confidential, proprietary copyrighted and/or legally privileged. It is intended solely for the use of the individual or entity named on this message. If you are not the intended recipient, and have received this message in error, please notify us immediately by reply e-mail and then delete it from your system.

Van: [email protected] [mailto:[email protected]] Namens Perez Ruixo, Juan Jose Verzonden: February 21, 2013 7:25 AM Aan: Ken Kowalski; 'Elassaiss - Schaap, J (Jeroen)'; 'Nick Holford'; 'nmusers' Onderwerp: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi All, The discussion below is valid when PD is not affecting PK. If PD affects PK, as in the case of some biologics, the sequential approaches may provide biased estimates, and the simultaneous fit is probably the best option. Regards, Juan. -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Ken Kowalski Sent: miércoles, 20 de febrero de 2013 9:1 To: 'Elassaiss - Schaap, J (Jeroen)'; 'Nick Holford'; 'nmusers' Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi Jeroen, I believe the feature that you describe for a simultaneous fit also applies to the PPP&D sequential approach that Nick advocates (which I also like). The framework of the PPP&D approach is to set it up the same as you would a simultaneous model fit but you fix the PK parameters (PK elements of theta, omega and sigma) to the final estimates from an independent fit to the PK data alone. It is a sequential approach that does not use the posthoc estimates of the PK parameters directly in the specification of the model as does the IPP sequential approach. The PPP&D approach by its sequential nature does not account for the correlation between the PK and PD parameter estimates. This can be a drawback or a feature of the approach depending on the level of model misspecification. When there is substantial PD model misspecification, a simultaneous model fit can lead to biased estimates of the PK parameters. The PPP&D approach guards against PD model misspecification impacting the PK parameter estimates since they are held fixed based on a separate (independent) fit to the PK data alone. Best, Ken Kenneth G. Kowalski President & CEO A2PG - Ann Arbor Pharmacometrics Group, Inc. 110 Miller Ave., Garden Suite Ann Arbor, MI 48104 Work: 734-274-8255 Cell: 248-207-5082 Fax: 734-913-0230 [email protected] www.a2pg.com -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Elassaiss - Schaap, J (Jeroen) Sent: Wednesday, February 20, 2013 8:38 AM To: Nick Holford; nmusers Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi, Let me clarify/call out one aspect that may not be obvious to all, it is underlying all the excellent replies (not to say the new abbreviations ;-). Shrinkage is only a problem in sequential analysis but not in simultaneous analysis. In sequential analysis one relies on posthoc estimates that are impacted by shrinkage; the simultaneous approach used variance estimates (omegas) which are not impacted. And, as others pointed out, PK shrinkage may not be the first aspect one needs to worry about in developing a PK-PD model but it is very useful to consider its potential impact in the more final stages of model development. Simultaneous analysis also has the benefit of accounting for correlation between PK and PD parameters and therefore provides a better handle for simulations in uncertainty. Best regards, Jeroen J. Elassaiss-Schaap Senior Principal Scientist Phone: + 31 412 66 9320 MSD | PK, PD and Drug Metabolism | Clinical PK-PD Mail stop KR 4406 | PO Box 20, 5340 BH Oss, NL -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Nick Holford Sent: Tuesday, February 19, 2013 2:16 To: nmusers Subject: Re: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Leonid, Thanks for pointing out the confusion in my response. I intended to write: "Sequential methods are preferred over simultaneous methods whenever there is a possibility of mis-specification of the model linking concentration to effect. This is nearly always a real risk so the simultaneous method is rarely a sensible choice (see Zhang et al part II). " The VIOA (very important old abbreviations http://www.cognigencorp.com/nonmem/nm/99dec192005.html) IPP, PPP&D are defined in the Zhang papers. Anybody who wants to do serious PKPD work should be familiar with these papers. The simulations performed by Zhang et al. show that simultaneous can be worse than sequential (see Part II paper). That is why I encourage a sequential approach using the PPP&D method. Best wishes, Nick On 19/02/2013 1:57 p.m., Leonid Gibiansky wrote: > Hi Nick, > I am afraid, many users are not very familiar with all the important > abbreviations - PPP&D, IPP, VINA, etc. :) > > I am not sure that I follow your points, looks contradictory: > > > Sequential methods are preferred .... > > ...sequential method is rarely a sensible choice > > In any case, I would not agree that PPP&D (use of both PK and PD data > in the simultaneous fit ?) is the only good method (or even that this > method is the best in all situations). In many cases, sequential > method gives nearly identical results to the simultaneous fit, and was > easier to implement numerically. I would be curious to see a good real > life example where sequential method provided the wrong answer while > PP&D was correct (that is, the example where these two methods > resulted in different clinically relevant conclusions). > > Leonid > > VINA = Very Important New Abbreviation > > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > > > On 2/18/2013 3:23 PM, Nick Holford wrote: >> Jakob, Leonid, >> >> I think it should be pointed out more clearly that PKPD analyses can >> be performed most simply using the PPP&D method. This method has >> better properties compared with IPP and is easier to set up and run >> (see Zhang et al Part I). Sequential methods are preferred over >> simultaneous methods whenever there is a possibility of >> mis-specification of the model linking concentration to effect. This >> is nearly always a real risk so the sequential method is rarely a >> sensible choice (see Zhang et al part II). >> >> The IPP+SE method has properties similar to PPP&D but it technically >> more challenging to use and cannot be used without a >> variance-covariance matrix of the estimates. This can usually only be >> obtained with oversimplified models (asymptotic $COV) or by time >> consuming bootstraps. >> >> The bottom line is to use PPP&D. >> >> Leonid makes a good point about using VPC for model evaluation. The >> VPC is capable of detecting structural model misspecification for >> PKPD analyses but it is not foolproof. A misspecified random effects >> model can compensate for a misspecified structural model. See >> references below for an example. >> >> Best wishes, >> >> Nick >> >> Karlsson MO, Holford NHG 2008. A Tutorial on Visual Predictive >> Checks. PAGE 17 (2008) Abstr 1434 [wwwpage-meetingorg/?abstract=1434] >> (last accessed 11 February 2012). >> >> http://holford.fmhs.auckland.ac.nz/docs/vpc-tutorial-and-datatop.pdf >> >> >> On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote: >>> Hi Matts, >>> >>> One method to investigate the problem would be to conduct VPC. If >>> VPC with model-estimated variances provides good (not inflated) >>> range of PK profiles then one can argue that the PK model provides >>> good description of the data and can be used for simulations >>> (including PK-PD). >>> >>> Another test could be to do VPCs for the PK-PD model: one with fixed >>> PK parameters (as was used in the sequential PK-PD modeling >>> procedure) and the other one with model-simulated ETAs for both PK >>> and PD parts. Again, if both provide good coverage of observed PK-PD >>> data then combination of PK and PD models can be trusted, and any of >>> the approaches can be applied. If one of the VPCs is inadequate, >>> than it should be noticeable in the too narrow or too wide >>> prediction intervals. >>> >>> Leonid >> >> On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote: >>> >>> Resending, since my posting from this morning (below) has not yet >>> appeared on nmusers. >>> >>> Apologies for any duplicate postings! >>> >>> *From:*Ribbing, Jakob *Sent:* 18 February 2013 09:59 *To:* "Kågedal, >>> Matts"; [email protected] *Cc:* Ribbing, Jakob >>> *Subject:* RE: Simulation settgin in the precence of Shrinkage in PK >>> when doing PK-PD analysis >>> >>> Hi Matts, >>> >>> I think you are correct; the problem you describe has not had much >>> (public) discussion. >>> >>> It is also correct like you say that this is mostly a problem in >>> case of all of the below >>> >>> ·sequential PK-PD analysis is applied (IPP approach, Zang et al) >>> >>> ·non-ignorable degree of shrinkage in PK parameters _of relevance_ >>> >>> oOf relevance: the PK parameters effectively driving PD for the >>> mechanism, e.g. CL/F if AUC is driving. In addition, if PD response >>> develops over several weeks/months then shrinkage in IOV may be >>> ignored even for relevant PK parameters >>> >>> ·I would also like to add that for this to be an issue individual PK >>> parameters must explain a fair degree of the variability in PD, >>> which is not always the case >>> >>> oIf driving PD with typical PK parameters (along with dose and other >>> PD covariates) does not increase PD omegas, compared to IPP, then >>> either PK shrinkage is already massive, or else it is not an issue >>> for the IPP-PD model >>> >>> If only the IPP approach is possible/practical a simplistic approach >>> to simulate PD data is as follows: >>> >>> ·sample (with replacement) the individual PK parameters along with >>> any potential covariates (maintaining correlation between IIP and >>> covariates, i.e. whole subject vectors for these entities, but >>> generally not for dose since generally should only have only random >>> association with IPP or PK/PD-covariates) >>> >>> ·then use the re-sampled datasets for simulating PD according to the >>> PD model (driven by IPP, covariates, dose, etc). The degree of >>> shrinkage is then the same for PD estimation and simulation. >>> >>> This approach may for example allow to simulate realistic PD >>> response at multiple dosing, based on only single dose PD. When the >>> MD data becomes available then one may find that variabilities shift >>> between PK and PD due to different PK shrinkage, but I would argue >>> the simulated PD responses still were realistic. This approach is >>> useful for predictions into the same population (especially if >>> sufficient number of subjects available for re-sampling), but may >>> not allow extrapolation into other populations where PK is projected >>> to be different. >>> >>> When possible the obvious solution is to apply one of the >>> alternative approaches to simultaneous PK-PD fit; after you have >>> arrived at a final-IPP model. >>> >>> If a simultaneous fit is obtainable/practical this is the best >>> option, but notice that e.g. if you have rich PK data in healthy and >>> no PK data in patients (plus PD data in both populations): You can >>> estimate separate omegas for PD parameters in healthy vs. >>> patients, but it may be difficult to tell whether patients higher PD >>> variability is due to PK shrinkage, or due to the actual PD >>> variability being higher in this population (or both). PD >>> variability may be confounded by a number of other factors that are >>> actually variability in PK (fu, active metabolites and bio phase >>> distribution, just to mention a few where information may be absent >>> on the individual level). Depending on the purpose of the modelling >>> this often not an issue, however. >>> >>> As you suggest there may be rare situations with IPP where a more >>> complicated approach is needed, with a) simulation and re-estimation >>> of PK model, to obtain Empirical-Bayes Estimates based on simulated >>> data, and then feed these into the subsequent PD model. I would see >>> this as a last resort. There are pitfalls in that if PD parameters >>> have been estimated under one degree of PK shrinkage, then applying >>> these estimates to a simulated example with different PK shrinkage >>> requires adjustment of PD variability. >>> I am not sure anyone has had to go down that route before and if not >>> I hope you do not have to either. Maybe others can advice on this? >>> >>> Best regards >>> >>> Jakob >>> >>> Two methodological references: >>> >>> Simultaneous vs. sequential analysis for population PK/PD data II: >>> robustness of methods. >>> >>> Zhang L, Beal SL, Sheiner LB. >>> >>> J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. >>> >>> Simultaneous vs. sequential analysis for population PK/PD data I: >>> best-case performance. >>> >>> Zhang L, Beal SL, Sheiner LB. >>> >>> J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404. >>> >> > e -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53 email: [email protected] http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford Notice: This e-mail message, together with any attachments, contains information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA 08889), and/or its affiliates Direct contact information for affiliates is available at http://www.merck.com/contact/contacts.html) that may be confidential, proprietary copyrighted and/or legally privileged. It is intended solely for the use of the individual or entity named on this message. If you are not the intended recipient, and have received this message in error, please notify us immediately by reply e-mail and then delete it from your system. ________________________________

On 21/02/2013 9:48 p.m., Eleveld, DJ wrote: > Hi All, > > The strange thing to me about the PPP&D method is that you generate two different > posthoc estimates for individual PK, one from the PK modelling alone and another from > the PPP&D step. > > Does anyone know if one of these inferior to the other? Which is the "right" > individual posthoc PK estimate? > > Warm regards, > > Douglas Eleveld > > -----Oorspronkelijk bericht----- > Van: [email protected] [mailto:[email protected]] Namens > Perez Ruixo, Juan Jose > Verzonden: February 21, 2013 7:25 AM > Aan: Ken Kowalski; 'Elassaiss - Schaap, J (Jeroen)'; 'Nick Holford'; 'nmusers' > Onderwerp: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in > PK when doing PK-PD analysis > > Hi All, > > The discussion below is valid when PD is not affecting PK. If PD affects PK, as > in the case of some biologics, the sequential approaches may provide biased > estimates, and the simultaneous fit is probably the best option. > > Regards, > Juan. > > -----Original Message----- > From: [email protected] [mailto:[email protected]] On > Behalf Of Ken Kowalski > Sent: miércoles, 20 de febrero de 2013 9:1 > To: 'Elassaiss - Schaap, J (Jeroen)'; 'Nick Holford'; 'nmusers' > Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in > PK when doing PK-PD analysis > > Hi Jeroen, > > I believe the feature that you describe for a simultaneous fit also applies to the PPP&D > sequential approach that Nick advocates (which I also like). The framework of the PPP&D > approach is to set it up the same as you would a simultaneous model fit but you fix the PK > parameters (PK elements of theta, omega and sigma) to the final estimates from an independent > fit to the PK data alone. It is a sequential approach that does not use the posthoc estimates > of the PK parameters directly in the specification of the model as does the IPP sequential > approach. The PPP&D approach by its sequential nature does not account for the correlation > between the PK and PD parameter estimates. This can be a drawback or a feature of the approach > depending on the level of model misspecification. When there is substantial PD model > misspecification, a simultaneous model fit can lead to biased estimates of the PK parameters. > The PPP&D approach guards against PD model misspecification impacting the PK parameter > estimates since they are held fixed based on a separate (independent) fit to the PK data alone. > > Best, > > Ken > > Kenneth G. Kowalski > President & CEO > A2PG - Ann Arbor Pharmacometrics Group, Inc. > 110 Miller Ave., Garden Suite > Ann Arbor, MI 48104 > Work: 734-274-8255 > Cell: 248-207-5082 > Fax: 734-913-0230 > [email protected] > www.a2pg.com > > -----Original Message----- > From: [email protected] [mailto:[email protected]] On > Behalf Of Elassaiss - Schaap, J (Jeroen) > Sent: Wednesday, February 20, 2013 8:38 AM > To: Nick Holford; nmusers > Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in > PK when doing PK-PD analysis > > Hi, > > Let me clarify/call out one aspect that may not be obvious to all, it is > underlying all the excellent replies (not to say the new abbreviations ;-). > > Shrinkage is only a problem in sequential analysis but not in simultaneous > analysis. In sequential analysis one relies on posthoc estimates that are > impacted by shrinkage; the simultaneous approach used variance estimates > (omegas) which are not impacted. > > And, as others pointed out, PK shrinkage may not be the first aspect one needs > to worry about in developing a PK-PD model but it is very useful to consider > its potential impact in the more final stages of model development. > Simultaneous analysis also has the benefit of accounting for correlation > between PK and PD parameters and therefore provides a better handle for > simulations in uncertainty. > > Best regards, > Jeroen > > J. Elassaiss-Schaap Senior Principal > Scientist Phone: + 31 412 66 9320 > MSD | PK, PD and Drug Metabolism | Clinical PK-PD Mail stop KR > 4406 | PO Box 20, 5340 BH Oss, NL > > -----Original Message----- > From: [email protected] [mailto:[email protected]] On > Behalf Of Nick Holford > Sent: Tuesday, February 19, 2013 2:16 > To: nmusers > Subject: Re: [NMusers] RE: Simulation settgin in the precence of Shrinkage in > PK when doing PK-PD analysis > > Leonid, > > Thanks for pointing out the confusion in my response. I intended to write: > > "Sequential methods are preferred over > simultaneous methods whenever there is a possibility of mis-specification of the > model linking concentration to effect. This is nearly always a real risk so the > simultaneous method is rarely a sensible choice (see Zhang et al part II). " > > The VIOA (very important old abbreviations > http://www.cognigencorp.com/nonmem/nm/99dec192005.html) IPP, PPP&D are > defined in the Zhang papers. Anybody who wants to do serious PKPD work should be familiar > with these papers. > > The simulations performed by Zhang et al. show that simultaneous can be worse than > sequential (see Part II paper). That is why I encourage a sequential approach using > the PPP&D method. > > Best wishes, > > Nick > > On 19/02/2013 1:57 p.m., Leonid Gibiansky wrote: > > > Hi Nick, > > I am afraid, many users are not very familiar with all the important > > abbreviations - PPP&D, IPP, VINA, etc. :) > > > > I am not sure that I follow your points, looks contradictory: > > > > > Sequential methods are preferred .... > > > ...sequential method is rarely a sensible choice > > > > In any case, I would not agree that PPP&D (use of both PK and PD data > > in the simultaneous fit ?) is the only good method (or even that this > > method is the best in all situations). In many cases, sequential > > method gives nearly identical results to the simultaneous fit, and was > > easier to implement numerically. I would be curious to see a good real > > life example where sequential method provided the wrong answer while > > PP&D was correct (that is, the example where these two methods > > resulted in different clinically relevant conclusions). > > > > Leonid > > > > VINA = Very Important New Abbreviation > > > > -------------------------------------- > > Leonid Gibiansky, Ph.D. > > President, QuantPharm LLC > > web: www.quantpharm.com > > e-mail: LGibiansky at quantpharm.com > > tel: (301) 767 5566 > > > > On 2/18/2013 3:23 PM, Nick Holford wrote: > > > > > Jakob, Leonid, > > > > > > I think it should be pointed out more clearly that PKPD analyses can > > > be performed most simply using the PPP&D method. This method has > > > better properties compared with IPP and is easier to set up and run > > > (see Zhang et al Part I). Sequential methods are preferred over > > > simultaneous methods whenever there is a possibility of > > > mis-specification of the model linking concentration to effect. This > > > is nearly always a real risk so the sequential method is rarely a > > > sensible choice (see Zhang et al part II). > > > > > > The IPP+SE method has properties similar to PPP&D but it technically > > > more challenging to use and cannot be used without a > > > variance-covariance matrix of the estimates. This can usually only be > > > obtained with oversimplified models (asymptotic $COV) or by time > > > consuming bootstraps. > > > > > > The bottom line is to use PPP&D. > > > > > > Leonid makes a good point about using VPC for model evaluation. The > > > VPC is capable of detecting structural model misspecification for > > > PKPD analyses but it is not foolproof. A misspecified random effects > > > model can compensate for a misspecified structural model. See > > > references below for an example. > > > > > > Best wishes, > > > > > > Nick > > > > > > Karlsson MO, Holford NHG 2008. A Tutorial on Visual Predictive > > > Checks. PAGE 17 (2008) Abstr 1434 [wwwpage-meetingorg/?abstract=1434] > > > (last accessed 11 February 2012). > > > > > > http://holford.fmhs.auckland.ac.nz/docs/vpc-tutorial-and-datatop.pdf > > > > > > On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote: > > > > > > > Hi Matts, > > > > > > > > One method to investigate the problem would be to conduct VPC. If > > > > VPC with model-estimated variances provides good (not inflated) > > > > range of PK profiles then one can argue that the PK model provides > > > > good description of the data and can be used for simulations > > > > (including PK-PD). > > > > > > > > Another test could be to do VPCs for the PK-PD model: one with fixed > > > > PK parameters (as was used in the sequential PK-PD modeling > > > > procedure) and the other one with model-simulated ETAs for both PK > > > > and PD parts. Again, if both provide good coverage of observed PK-PD > > > > data then combination of PK and PD models can be trusted, and any of > > > > the approaches can be applied. If one of the VPCs is inadequate, > > > > than it should be noticeable in the too narrow or too wide > > > > prediction intervals. > > > > > > > > Leonid > > > > > > On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote: > > > > > > > Resending, since my posting from this morning (below) has not yet > > > > appeared on nmusers. > > > > > > > > Apologies for any duplicate postings! > > > > > > > > *From:*Ribbing, Jakob *Sent:* 18 February 2013 09:59 *To:* "Kågedal, > > > > Matts"; [email protected] *Cc:* Ribbing, Jakob > > > > *Subject:* RE: Simulation settgin in the precence of Shrinkage in PK > > > > when doing PK-PD analysis > > > > > > > > Hi Matts, > > > > > > > > I think you are correct; the problem you describe has not had much > > > > (public) discussion. > > > > > > > > It is also correct like you say that this is mostly a problem in > > > > case of all of the below > > > > > > > > ·sequential PK-PD analysis is applied (IPP approach, Zang et al) > > > > > > > > ·non-ignorable degree of shrinkage in PK parameters _of relevance_ > > > > > > > > oOf relevance: the PK parameters effectively driving PD for the > > > > mechanism, e.g. CL/F if AUC is driving. In addition, if PD response > > > > develops over several weeks/months then shrinkage in IOV may be > > > > ignored even for relevant PK parameters > > > > > > > > ·I would also like to add that for this to be an issue individual PK > > > > parameters must explain a fair degree of the variability in PD, > > > > which is not always the case > > > > > > > > oIf driving PD with typical PK parameters (along with dose and other > > > > PD covariates) does not increase PD omegas, compared to IPP, then > > > > either PK shrinkage is already massive, or else it is not an issue > > > > for the IPP-PD model > > > > > > > > If only the IPP approach is possible/practical a simplistic approach > > > > to simulate PD data is as follows: > > > > > > > > ·sample (with replacement) the individual PK parameters along with > > > > any potential covariates (maintaining correlation between IIP and > > > > covariates, i.e. whole subject vectors for these entities, but > > > > generally not for dose since generally should only have only random > > > > association with IPP or PK/PD-covariates) > > > > > > > > ·then use the re-sampled datasets for simulating PD according to the > > > > PD model (driven by IPP, covariates, dose, etc). The degree of > > > > shrinkage is then the same for PD estimation and simulation. > > > > > > > > This approach may for example allow to simulate realistic PD > > > > response at multiple dosing, based on only single dose PD. When the > > > > MD data becomes available then one may find that variabilities shift > > > > between PK and PD due to different PK shrinkage, but I would argue > > > > the simulated PD responses still were realistic. This approach is > > > > useful for predictions into the same population (especially if > > > > sufficient number of subjects available for re-sampling), but may > > > > not allow extrapolation into other populations where PK is projected > > > > to be different. > > > > > > > > When possible the obvious solution is to apply one of the > > > > alternative approaches to simultaneous PK-PD fit; after you have > > > > arrived at a final-IPP model. > > > > > > > > If a simultaneous fit is obtainable/practical this is the best > > > > option, but notice that e.g. if you have rich PK data in healthy and > > > > no PK data in patients (plus PD data in both populations): You can > > > > estimate separate omegas for PD parameters in healthy vs. > > > > patients, but it may be difficult to tell whether patients higher PD > > > > variability is due to PK shrinkage, or due to the actual PD > > > > variability being higher in this population (or both). PD > > > > variability may be confounded by a number of other factors that are > > > > actually variability in PK (fu, active metabolites and bio phase > > > > distribution, just to mention a few where information may be absent > > > > on the individual level). Depending on the purpose of the modelling > > > > this often not an issue, however. > > > > > > > > As you suggest there may be rare situations with IPP where a more > > > > complicated approach is needed, with a) simulation and re-estimation > > > > of PK model, to obtain Empirical-Bayes Estimates based on simulated > > > > data, and then feed these into the subsequent PD model. I would see > > > > this as a last resort. There are pitfalls in that if PD parameters > > > > have been estimated under one degree of PK shrinkage, then applying > > > > these estimates to a simulated example with different PK shrinkage > > > > requires adjustment of PD variability. > > > > I am not sure anyone has had to go down that route before and if not > > > > I hope you do not have to either. Maybe others can advice on this? > > > > > > > > Best regards > > > > > > > > Jakob > > > > > > > > Two methodological references: > > > > > > > > Simultaneous vs. sequential analysis for population PK/PD data II: > > > > robustness of methods. > > > > > > > > Zhang L, Beal SL, Sheiner LB. > > > > > > > > J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. > > > > > > > > Simultaneous vs. sequential analysis for population PK/PD data I: > > > > best-case performance. > > > > > > > > Zhang L, Beal SL, Sheiner LB. > > > > > > > > J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404. > > > > e > > -- > Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical > Pharmacology, Bldg 503 Room 302A University of Auckland,85 Park Rd,Private Bag > 92019,Auckland,New Zealand tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 > 23 53 > email: [email protected] > http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford > > Notice: This e-mail message, together with any attachments, contains information > of Merck & Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA > 08889), and/or its affiliates Direct contact information for affiliates is > available at > http://www.merck.com/contact/contacts.html) that may be confidential, > proprietary copyrighted and/or legally privileged. It is intended solely for > the use of the individual or entity named on this message. If you are not the > intended recipient, and have received this message in error, please notify us > immediately by reply e-mail and then delete it from your system. > > ________________________________ >

-----Original Message----- From: Eleveld, DJ [mailto:[email protected]] Sent: Thursday, February 21, 2013 3:48 AM To: 'Perez Ruixo, Juan Jose'; Ken Kowalski; 'Elassaiss - Schaap, J (Jeroen)'; 'Nick Holford'; 'nmusers' Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi All, The strange thing to me about the PPP&D method is that you generate two different posthoc estimates for individual PK, one from the PK modelling alone and another from the PPP&D step. Does anyone know if one of these inferior to the other? Which is the "right" individual posthoc PK estimate? Warm regards, Douglas Eleveld -----Oorspronkelijk bericht----- Van: [email protected] [mailto:[email protected]] Namens Perez Ruixo, Juan Jose Verzonden: February 21, 2013 7:25 AM Aan: Ken Kowalski; 'Elassaiss - Schaap, J (Jeroen)'; 'Nick Holford'; 'nmusers' Onderwerp: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi All, The discussion below is valid when PD is not affecting PK. If PD affects PK, as in the case of some biologics, the sequential approaches may provide biased estimates, and the simultaneous fit is probably the best option. Regards, Juan. -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Ken Kowalski Sent: miércoles, 20 de febrero de 2013 9:1 To: 'Elassaiss - Schaap, J (Jeroen)'; 'Nick Holford'; 'nmusers' Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi Jeroen, I believe the feature that you describe for a simultaneous fit also applies to the PPP&D sequential approach that Nick advocates (which I also like). The framework of the PPP&D approach is to set it up the same as you would a simultaneous model fit but you fix the PK parameters (PK elements of theta, omega and sigma) to the final estimates from an independent fit to the PK data alone. It is a sequential approach that does not use the posthoc estimates of the PK parameters directly in the specification of the model as does the IPP sequential approach. The PPP&D approach by its sequential nature does not account for the correlation between the PK and PD parameter estimates. This can be a drawback or a feature of the approach depending on the level of model misspecification. When there is substantial PD model misspecification, a simultaneous model fit can lead to biased estimates of the PK parameters. The PPP&D approach guards against PD model misspecification impacting the PK parameter estimates since they are held fixed based on a separate (independent) fit to the PK data alone. Best, Ken Kenneth G. Kowalski President & CEO A2PG - Ann Arbor Pharmacometrics Group, Inc. 110 Miller Ave., Garden Suite Ann Arbor, MI 48104 Work: 734-274-8255 Cell: 248-207-5082 Fax: 734-913-0230 [email protected] www.a2pg.com -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Elassaiss - Schaap, J (Jeroen) Sent: Wednesday, February 20, 2013 8:38 AM To: Nick Holford; nmusers Subject: RE: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Hi, Let me clarify/call out one aspect that may not be obvious to all, it is underlying all the excellent replies (not to say the new abbreviations ;-). Shrinkage is only a problem in sequential analysis but not in simultaneous analysis. In sequential analysis one relies on posthoc estimates that are impacted by shrinkage; the simultaneous approach used variance estimates (omegas) which are not impacted. And, as others pointed out, PK shrinkage may not be the first aspect one needs to worry about in developing a PK-PD model but it is very useful to consider its potential impact in the more final stages of model development. Simultaneous analysis also has the benefit of accounting for correlation between PK and PD parameters and therefore provides a better handle for simulations in uncertainty. Best regards, Jeroen J. Elassaiss-Schaap Senior Principal Scientist Phone: + 31 412 66 9320 MSD | PK, PD and Drug Metabolism | Clinical PK-PD Mail stop KR 4406 | PO Box 20, 5340 BH Oss, NL -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Nick Holford Sent: Tuesday, February 19, 2013 2:16 To: nmusers Subject: Re: [NMusers] RE: Simulation settgin in the precence of Shrinkage in PK when doing PK-PD analysis Leonid, Thanks for pointing out the confusion in my response. I intended to write: "Sequential methods are preferred over simultaneous methods whenever there is a possibility of mis-specification of the model linking concentration to effect. This is nearly always a real risk so the simultaneous method is rarely a sensible choice (see Zhang et al part II). " The VIOA (very important old abbreviations http://www.cognigencorp.com/nonmem/nm/99dec192005.html) IPP, PPP&D are defined in the Zhang papers. Anybody who wants to do serious PKPD work should be familiar with these papers. The simulations performed by Zhang et al. show that simultaneous can be worse than sequential (see Part II paper). That is why I encourage a sequential approach using the PPP&D method. Best wishes, Nick On 19/02/2013 1:57 p.m., Leonid Gibiansky wrote: > Hi Nick, > I am afraid, many users are not very familiar with all the important > abbreviations - PPP&D, IPP, VINA, etc. :) > > I am not sure that I follow your points, looks contradictory: > > > Sequential methods are preferred .... > > ...sequential method is rarely a sensible choice > > In any case, I would not agree that PPP&D (use of both PK and PD data > in the simultaneous fit ?) is the only good method (or even that this > method is the best in all situations). In many cases, sequential > method gives nearly identical results to the simultaneous fit, and was > easier to implement numerically. I would be curious to see a good real > life example where sequential method provided the wrong answer while > PP&D was correct (that is, the example where these two methods > resulted in different clinically relevant conclusions). > > Leonid > > VINA = Very Important New Abbreviation > > > -------------------------------------- > Leonid Gibiansky, Ph.D. > President, QuantPharm LLC > web: www.quantpharm.com > e-mail: LGibiansky at quantpharm.com > tel: (301) 767 5566 > > > > On 2/18/2013 3:23 PM, Nick Holford wrote: >> Jakob, Leonid, >> >> I think it should be pointed out more clearly that PKPD analyses can >> be performed most simply using the PPP&D method. This method has >> better properties compared with IPP and is easier to set up and run >> (see Zhang et al Part I). Sequential methods are preferred over >> simultaneous methods whenever there is a possibility of >> mis-specification of the model linking concentration to effect. This >> is nearly always a real risk so the sequential method is rarely a >> sensible choice (see Zhang et al part II). >> >> The IPP+SE method has properties similar to PPP&D but it technically >> more challenging to use and cannot be used without a >> variance-covariance matrix of the estimates. This can usually only be >> obtained with oversimplified models (asymptotic $COV) or by time >> consuming bootstraps. >> >> The bottom line is to use PPP&D. >> >> Leonid makes a good point about using VPC for model evaluation. The >> VPC is capable of detecting structural model misspecification for >> PKPD analyses but it is not foolproof. A misspecified random effects >> model can compensate for a misspecified structural model. See >> references below for an example. >> >> Best wishes, >> >> Nick >> >> Karlsson MO, Holford NHG 2008. A Tutorial on Visual Predictive >> Checks. PAGE 17 (2008) Abstr 1434 [wwwpage-meetingorg/?abstract=1434] >> (last accessed 11 February 2012). >> >> http://holford.fmhs.auckland.ac.nz/docs/vpc-tutorial-and-datatop.pdf >> >> >> On 19/02/2013 7:55 a.m., Leonid Gibiansky wrote: >>> Hi Matts, >>> >>> One method to investigate the problem would be to conduct VPC. If >>> VPC with model-estimated variances provides good (not inflated) >>> range of PK profiles then one can argue that the PK model provides >>> good description of the data and can be used for simulations >>> (including PK-PD). >>> >>> Another test could be to do VPCs for the PK-PD model: one with fixed >>> PK parameters (as was used in the sequential PK-PD modeling >>> procedure) and the other one with model-simulated ETAs for both PK >>> and PD parts. Again, if both provide good coverage of observed PK-PD >>> data then combination of PK and PD models can be trusted, and any of >>> the approaches can be applied. If one of the VPCs is inadequate, >>> than it should be noticeable in the too narrow or too wide >>> prediction intervals. >>> >>> Leonid >> >> On 19/02/2013 5:20 a.m., Ribbing, Jakob wrote: >>> >>> Resending, since my posting from this morning (below) has not yet >>> appeared on nmusers. >>> >>> Apologies for any duplicate postings! >>> >>> *From:*Ribbing, Jakob *Sent:* 18 February 2013 09:59 *To:* "Kågedal, >>> Matts"; [email protected] *Cc:* Ribbing, Jakob >>> *Subject:* RE: Simulation settgin in the precence of Shrinkage in PK >>> when doing PK-PD analysis >>> >>> Hi Matts, >>> >>> I think you are correct; the problem you describe has not had much >>> (public) discussion. >>> >>> It is also correct like you say that this is mostly a problem in >>> case of all of the below >>> >>> ·sequential PK-PD analysis is applied (IPP approach, Zang et al) >>> >>> ·non-ignorable degree of shrinkage in PK parameters _of relevance_ >>> >>> oOf relevance: the PK parameters effectively driving PD for the >>> mechanism, e.g. CL/F if AUC is driving. In addition, if PD response >>> develops over several weeks/months then shrinkage in IOV may be >>> ignored even for relevant PK parameters >>> >>> ·I would also like to add that for this to be an issue individual PK >>> parameters must explain a fair degree of the variability in PD, >>> which is not always the case >>> >>> oIf driving PD with typical PK parameters (along with dose and other >>> PD covariates) does not increase PD omegas, compared to IPP, then >>> either PK shrinkage is already massive, or else it is not an issue >>> for the IPP-PD model >>> >>> If only the IPP approach is possible/practical a simplistic approach >>> to simulate PD data is as follows: >>> >>> ·sample (with replacement) the individual PK parameters along with >>> any potential covariates (maintaining correlation between IIP and >>> covariates, i.e. whole subject vectors for these entities, but >>> generally not for dose since generally should only have only random >>> association with IPP or PK/PD-covariates) >>> >>> ·then use the re-sampled datasets for simulating PD according to the >>> PD model (driven by IPP, covariates, dose, etc). The degree of >>> shrinkage is then the same for PD estimation and simulation. >>> >>> This approach may for example allow to simulate realistic PD >>> response at multiple dosing, based on only single dose PD. When the >>> MD data becomes available then one may find that variabilities shift >>> between PK and PD due to different PK shrinkage, but I would argue >>> the simulated PD responses still were realistic. This approach is >>> useful for predictions into the same population (especially if >>> sufficient number of subjects available for re-sampling), but may >>> not allow extrapolation into other populations where PK is projected >>> to be different. >>> >>> When possible the obvious solution is to apply one of the >>> alternative approaches to simultaneous PK-PD fit; after you have >>> arrived at a final-IPP model. >>> >>> If a simultaneous fit is obtainable/practical this is the best >>> option, but notice that e.g. if you have rich PK data in healthy and >>> no PK data in patients (plus PD data in both populations): You can >>> estimate separate omegas for PD parameters in healthy vs. >>> patients, but it may be difficult to tell whether patients higher PD >>> variability is due to PK shrinkage, or due to the actual PD >>> variability being higher in this population (or both). PD >>> variability may be confounded by a number of other factors that are >>> actually variability in PK (fu, active metabolites and bio phase >>> distribution, just to mention a few where information may be absent >>> on the individual level). Depending on the purpose of the modelling >>> this often not an issue, however. >>> >>> As you suggest there may be rare situations with IPP where a more >>> complicated approach is needed, with a) simulation and re-estimation >>> of PK model, to obtain Empirical-Bayes Estimates based on simulated >>> data, and then feed these into the subsequent PD model. I would see >>> this as a last resort. There are pitfalls in that if PD parameters >>> have been estimated under one degree of PK shrinkage, then applying >>> these estimates to a simulated example with different PK shrinkage >>> requires adjustment of PD variability. >>> I am not sure anyone has had to go down that route before and if not >>> I hope you do not have to either. Maybe others can advice on this? >>> >>> Best regards >>> >>> Jakob >>> >>> Two methodological references: >>> >>> Simultaneous vs. sequential analysis for population PK/PD data II: >>> robustness of methods. >>> >>> Zhang L, Beal SL, Sheiner LB. >>> >>> J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):405-16. >>> >>> Simultaneous vs. sequential analysis for population PK/PD data I: >>> best-case performance. >>> >>> Zhang L, Beal SL, Sheiner LB. >>> >>> J Pharmacokinet Pharmacodyn. 2003 Dec;30(6):387-404. >>> >> > e -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53 email: [email protected] http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford Notice: This e-mail message, together with any attachments, contains information of Merck & Co., Inc. 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