Re: IOV effects

Date: Tue, 23 Nov 1999 14:30:13 +0100 From: Pascal Girard <pg@upcl.univ-lyon1.fr> Subject: Re: IOV effects Hi Mats and James, I understand James question "should the patients with only one occasion have an IOV random effect?" as "When I have a dataset mixing patients with several occasions, and other with only one occasion, do I have to implement an IOV model within NONMEM for every patients, OR can I implement it only for patients with several occasions and leave those with one occasion with only IIV?" Mats says that in this case you should always have an IOV random effect. I think that before answering about the principle, we should answer, from a technical point of view, that we are left with little choice: either you implement IIV and IOV for every patient or you implement only IIV for every patient. The thing that you can have in one model is parameters affected by IIV & IOV for <<every>> patients and other parameters affected <<only>> by IIV for <<every>> patients. It would be tricky <<and>> useless to have in one model a parameter affected by IIV & IOV for a subset of patients with several occasions, and IIV only for another subset of patients with only one occasion. This would be useless because of the way IOV is modelled: when a patient has only one occasion he carries absolutely no information about IOV. This is true whateve method you use (NONMEM, NLME Splus function, MCMC, ...). With NONMEM, if you try to estimate IOV using a dataset with patients having only one occasion, you should get an estimate of IOV omega close to 1E-8, which is the sign that NONMEM has not enough information to estimate this random effect (we found this when estimating inter-study variability -paper in press in JPS-, and I think that what Mats found in his 1993 JPB paper). You have the same difficulties when you try to separate IIV from residual variability with a dataset where you have only one observation/patient. This can only be done by preliminary fixing residual variability to a certain value using a parametric model. This is currently performed with non-parametric methods as NPML or NPEM. If you are confident with some prior knowledge about IOV, maybe you can try to fix IOV to a certain value. Now what happens when you estimate IOV on a dataset with some patients with several occasions and most with only one occasion? My guess is that you will have to pay some attention to the precision with which random IOV effect parameters are estimated: the less number of patients with several occasions you have in your dataset, the more imprecisely estimated those parameters will be. And you may even find IOV useless in certain cases, except ... if you trust IOV exists and set a prior distribution for it. But this takes us back to the previous discussion ... Pascal -- Pascal Girard ------------------------------------------------------------------ Service Pharmacologie Clinique PG@upcl.univ-lyon1.fr Faculte RTH Laennec Tel : +33 (0)4 78 78 57 26 BP 8071, rue Guillaume Paradin Fax : +33 (0)4 78 77 69 17 69376 LYON Cedex 08 FRANCE http://www.spc.univ-lyon1.fr