RE: Rational of using IOV

From: mats karlsson [mailto:[email protected]]<mailto:[mailto:[email protected]]> Sent: Tuesday, 2 November 2010 3:11 p.m. To: Stephen Duffull; 'Nick Holford'; [email protected]<mailto:[email protected]> Subject: RE: [NMusers] Rational of using IOV Hi Steve, I think we can apply some mechanistic insight into IOV in most cases. For example for absorption parameters, it is difficult to see that anything but each dosing occasion would constitute a separate occasion. There may however be situations where it is difficult to judge properly and IOV may not be modeled ideally. Then however, not only IOV, but also IIV and RV become "nuisance" parameters as they will not represent the true IIV or RV. Best regards, Mats Mats Karlsson, PhD Professor of Pharmacometrics Dept of Pharmaceutical Biosciences Uppsala University Sweden Postal address: Box 591, 751 24 Uppsala, Sweden Phone +46 18 4714105 Fax + 46 18 4714003 From: [email protected]<mailto:[email protected]> [mailto:[email protected]]<mailto:[mailto:[email protected]]> On Behalf Of Stephen Duffull Sent: Monday, November 01, 2010 10:49 PM To: Nick Holford; [email protected]<mailto:[email protected]> Subject: RE: [NMusers] Rational of using IOV Nick While I agree that BOV is not solely a nuisance parameter it is a design specific parameter and hence can be somewhat of a nuisance. By design specific we can formulate settings in which the design of the study changes the estimate of BOV. To estimate the variance between occasions the duration of the occasion needs to be defined (a priori). If the occasion is long then the estimate of BOV will tend to zero since the integral over the occasion to get the average parameter value will integrate over the random variability. If the occasion is short then it will tend to a larger positive number. Imagine an occasion of 1 hour versus 1 year. I realise that most tend to use a dose interval as an occasion but this is also arbitrary as is clinic visits. The duration of the occasion would need to be indexed to the substantive inferences of the model to ensure that any influence that BOV has can be assessed in terms of model predictions. Given that BOV is design specific then how should this be interpreted in any given circumstance? Note that being design specific doesn't preclude the benefit of BOV in its role as an estimable but nuisance parameter (i.e. to reduce bias in estimates of the population mean parameter values). Steve -- Professor Stephen Duffull Chair of Clinical Pharmacy School of Pharmacy University of Otago PO Box 56 Dunedin New Zealand E: [email protected]<mailto:[email protected]> P: +64 3 479 5044 F: +64 3 479 7034 W: http://pharmacy.otago.ac.nz/profiles/stephenduffull Design software: http://www.winpopt.com From: [email protected]<mailto:[email protected]> [mailto:[email protected]]<mailto:[mailto:[email protected]]> On Behalf Of Nick Holford Sent: Tuesday, 2 November 2010 8:33 a.m. To: [email protected]<mailto:[email protected]> Subject: Re: [NMusers] Rational of using IOV Thierry, Between subject variability (BSV aka IIV) and within subject variability (WSV aka IOV) describe the limits of what we can identify as sources of variability. I don't consider this a nuisance -- it is an opportunity for learning. The random assumption used for estimation of WSV is a convenient way of describing the size of the problem. If we recognize there is a large element of WSV then it may stimulate thinking and further investigation to try and understand it. Ignoring WSV will give a false impression about what can be gained from TCI (aka TDM). TCI can only hope to remove the BSV part of unpredictable variability. See Holford NH. Target concentration intervention: beyond Y2K. Br J Clin Pharmacol. 1999;48(1):9-13. Best wishes, Nick On 2/11/2010 2:34 a.m., Buclin Thierry wrote: Dear James, I always thought that intra-individual variability (IIV) classically represented the immovable limit on the gains to be expected from TDM - IOV being indeed used only in a minority of population PK analyses. Both intra- and inter-occasion variability actually represent nuisance. We agree on the point that specifying an IOV term in a model will decrease the residual IIV. But wouldn't this precisely give a falsely favorable impression about potential gains from a TDM program? Am I wrong to think so? Kind regards Thierry De : James G Wright [mailto:[email protected]] Envoyé : lundi, 1 novembre 2010 14:04 À : Buclin Thierry Objet : Re: [NMusers] Rational of using IOV Dear Thierry, I hope you are well. I think you are right to highlight the importance of IOV for TDM, but I would argue it is very important to include it in the model. This is because IOV places an immovable limit on the gains from TDM. The classic error is to develop a TDM strategy mistakenly lumping IOV with IIV, and drastically over-estimating the utility of TDM. Best regards, James On 01/11/2010 11:55, Buclin Thierry wrote: Hi Nicolas My short answer would be another question: "what is the aim of your analysis ?" IOV is fine to split variability into inter-individual, intra-individual-inter-occasion and intra-individual-intra-occasion random components. This is excellent for data description, and can bring interesting insight into the mechanisms explaining variability. But if you want to use your results for prediction, e.g. to devise a TDM program, you won't be able to draw relevant information from IOV: a blood sample obtained in a patient on a certain occasion won't inform you on your patient's behavior on another occasion; in this situation, a model merely distinguishing inter-individual and intra-individual variability components is easier to exploit. Thus, there may be good reasons not to use IOV even when it would be possible. Kind regards Thierry Thierry Buclin, MD, PD, Division of Clinical Pharmacology and Toxicology University Hospital of Lausanne (CHUV) Lausanne - SWITZERLAND tel +41 21 314 42 61 fax +41 21 314 42 66 On 1/11/2010 10:53 a.m., Nicolas SIMON wrote: Dear colleagues, could someone give me an advice about the rational of using IOV in a particular circumstance? We have data from a clin trial with 3 occasions for each patient. It was a chemotherapy and the patients have received up to 7 cures. The issue is that the 3 occasions differ from one patient to another. Patient X may have be seen on cure 3, 5 and 7 while patient X+1 was seen on cure 2, 5 and 6 or whatever... It seems to me that combining the 1st occ of all patients is meaningless (as for 2nd and 3rd). Shall we use as many occasions as cures (7 in our dataset)? In that case, how many patients by occ is relevant as a minimum? For certain occ we may have few patients. Combining cures is hazardous and has no clinical justification. Best regards Nicolas Pr Nicolas SIMON Universite de la Mediterranee (Aix-Marseille II) -- James G Wright PhD, Scientist, Wright Dose Ltd Tel: UK (0)772 5636914 -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology 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]<mailto:[email protected]> http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford