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

-----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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