RE: WRES AND OUTLIER IDENTIFICATION/EXCLUSION

From: "Kowalski, Ken" Ken.Kowalski@pfizer.com Subject: RE: [NMusers] WRES AND OUTLIER IDENTIFICATION/EXCLUSION Date: Tue, 3 Oct 2006 17:31:45 -0400 Mats, Outlier detection requires some criteria for explicit assessment. In practice we often look at residual plots and make a judgement as to whether to classify an observation as an outlier or non-outlier. It is important to make the distinction that this classification is done only for diagnostic purposes. No matter how we choose to deal with outliers, we should be explicit, transparent, and systematic about our procedures for handling data outliers. Moreover, as we stated previously, we need to first rule out to the best of our ability any model misspecification before we take any actions with regards to outliers we detect (e.g., excluding outlier observations or fitting alternative models and down-weighting). We could use a similar approach to classify data outliers based on this full likelihood (residual error mixture model) to what NONMEM does with the MIXEST calculation to classify which subpopulation each subject belongs to. It is purely a post hoc calculation for diagnostic purposes; knowing which subpopulation each observation belongs to is not involved in the estimation. In this case it's just not built into NONMEM so we would have to do some post-processing to perform this post hoc classification. At this point we are certainly not recommending this interesting but untested approach. But it may be promising and worth evaluating with real data and simulation studies. Figuring out the post hoc calculations would certainly help improve the diagnostic value of this approach as well. I trust that you had or are having an enjoyable vacation. Best wishes, Ken