RE: msg from atul

From: "Piotrovskij, Vladimir [JanBe]" <VPIOTROV@janbe.jnj.com> Subject: RE: msg from atul Date: Wed, 15 Nov 2000 08:53:15 +0100 Atul, Concerning outliers: if your drug is metabolized and CYP2D6-dependent you might assume a subpopulation of 'poor metabolizers'. In this case, implementing a mixture of two (log-)normals for CL may help. If polymorphic metabolism in not an issue these 'outliers' ('outliers', not real outliers) may be the result of an obvious deviation of concentration distribution from normality. If you apply a so-called 'transform-both-side' approach and fit the model to the logarithm of concentrations (model prediction, F, should be transformed accordingly) you will most probably get rid of the 'outliers'. The residual error for log-transformed concentrations will be a simple additive one, and no INTERACTION will be needed, too. Best regards, Vladimir ---------------------------------------------------------------------- Vladimir Piotrovsky, Ph.D. Janssen Research Foundation Clinical Pharmacokinetics (ext. 5463) B-2340 Beerse Belgium Email: vpiotrov@janbe.jnj.com From: Mats Karlsson <Mats.Karlsson@biof.uu.se> Subject: Re: msg from atul Date: Wed, 15 Nov 2000 19:06:50 +0100 Dear Lewis, As part of Ulrika Wählby's work on actual versus nominal significance levels in NONMEM it became clear (we think) that whilst FO provides upwards biased significance levels FOCE+INTERACTION did not (roughly speaking). As you pointed out FOCE may be very time-consuming so we did investigate the GLS approach with FO as an quick alternative. Unfortunately, although it did improve things compared to FO, it was not as good as FOCE+INTER, and not good enough to say that nominal and actual significance levels agreed. However, that's not to say that for other purposes the idea may not be good (after all it did better than standard FO). Just one note, if you use IPRED as W and have sparse data, it may be almost the same as weighting against the observed concentration. Therefore I would not use it unless I had a decent number of observations per subject (which is the circumstance we studied). You may with sparse data actually want to use PRED from a previous run rather than IPRED. Best regards, Mats -- Mats Karlsson, PhD Professor of Biopharmaceutics and Pharmacokinetics Div. of Biopharmaceutics and Pharmacokinetics Dept of Pharmacy Faculty of Pharmacy Uppsala University Box 580 SE-751 23 Uppsala Sweden phone +46 18 471 4105 fax +46 18 471 4003 mats.karlsson@biof.uu.se