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

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. 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