RE: WRES AND OUTLIER IDENTIFICATION/EXCLUSION

From: "Mats Karlsson" mats.karlsson@farmbio.uu.se Subject: RE: [NMusers] WRES AND OUTLIER IDENTIFICATION/EXCLUSION Date: Wed, 27 Sep 2006 23:54:18 +0200 Ken, Regarding your comments: 1) I agree. According to the model I suggested, a single outlying data point would mean that the entire information content of that individual would be considered less than without that outlier. Of course this model makes assumptions too, even if it relaxes the assumption of everyone having the same residual variability. It still makes the assumption that residual error distribution is a (transformation of) normal distribution. 2) Maybe the idea has merits. Trying it and showing that the extra subjectivity and effort does pay off in terms of increased parameter precision is however something that I think needs to be shown. Also, with your approach I would think that even when you do identify the outliers correctly, the assumption of a normally distributed random error for the outliers is usually not appropriate. In my experience that is not what outliers/errors look like. E.g. often some observations are far too high (sampling in the wrong arm) or far too low (didn't take the dose), but rarely do the two equate to form a nice normal. Further, I'm not sure what you mean by "prespecified criteria". This could be tricky as outliers are usually not easy to foresee. Your suggestion seems to imply that these are identified before you fit a model to the data and then it is even harder to predict which are outliers. Last, it is not uncommon that one can see that one out of two data points are an outlier, but difficult to determine which of the two it is. 3) I tend to agree, but IWRES is not a panacea either. If data are sparse (compared to the number of parameters and especially etas), IWRES can be quite misleading due to overfit. 4) Your usual good advice that I would not want to disagree with. In relation to this, one of my former co-workers (Dr Sima Sadrai) reminded me in a mail that I think was intended for nmusers (copied below), that there may be some further help in inspecting the individual contribution to the likelihood. The idea is to investigate whether some individuals are driving or masking any model selection. The main idea was not in relation to errors/outlying data points, but maybe it has some merits there too. 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