Dear Thierry,
The article below nicely describes the importance of incorporating IOV since it otherwise might lead to a falsely optimistic impression of the potential value of TDM. Karlsson MO, Sheiner LB. The importance of modeling interoccasion variability in population pharmacokinetic analyses. J Pharmacokinet Biopharm. 1993 Dec;21(6):735-50. Best regards, Ulrika Ulrika Simonsson, PhD Assoc Prof of Pharmacometrics Uppsala Pharmacometrics Department of Pharmaceutical Biosciences Uppsala University BMC, Box 591, 751 24 Uppsala Sweden From: [email protected] [mailto:[email protected]] On Behalf Of Buclin Thierry Sent: den 1 november 2010 14:35 To: [email protected] Subject: RE: [NMusers] Rational of using IOV 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 wouldnt 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 wont be able to draw relevant information from IOV: a blood sample obtained in a patient on a certain occasion wont inform you on your patients 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
