RE: WRES AND OUTLIER IDENTIFICATION/EXCLUSION

From: Mats Karlsson [mailto:mats.karlsson@farmbio.uu.se] Subject: RE: [NMusers] WRES AND OUTLIER IDENTIFICATION/EXCLUSION Date: Monday, October 02, 2006 5:08 PM Hi Ken, Allowing varying residual variability between subjects provide you with an estimate of which individuals' data for which the model doesn't fit, whether it is through error, outlier or model misspecification. If you use a prespecified criteria, I don't think this will change - your criteria (if it is any good) will still identify observations for which the model doesn't fit. These will represent errors, outliers or data where model misspecification is pronounced. I'm not sure why you think you could, through the somewhat ad hoc procedure of e.g. IWRES>x strike a better balance and only get errors and not model misspecification. Regarding classification of data points as outliers or not, I guess classification is always destroying information. Why would you want to classify? However, I guess that you could get the information to classified by "just" evaluating the OFV for the 2**Ni possibilities, where Ni is the number of observations for subject i. On the practical side, a drawback of the individual observation mixture model is of course that it requires the LAPLACIAN method (unless Matt has pulled off another stunt!). Thanks for the entertaining discussion, but tomorrow morning I'm off for vacation so don't expect any quick reply on further comments. Best regards, Mats Mats Karlsson, PhD Professor of Pharmacometrics Div. of Pharmacokinetics and Drug Therapy Dept. of Pharmaceutical Biosciences Faculty of Pharmacy Uppsala University Box 591 SE-751 24 Uppsala Sweden phone +46 18 471 4105 fax +46 18 471 4003 mats.karlsson@farmbio.uu.se