RE: WRES AND OUTLIER IDENTIFICATION/EXCLUSION

From: Leonid Gibiansky leonidg@metrumrg.com Subject: RE: [NMusers] WRES AND OUTLIER IDENTIFICATION/EXCLUSION Date: Tue, 26 Sep 2006 09:16:31 -0400 It is known that methods implemented by NONMEM are not robust to the outliers (or data errors). Few bad observations can significantly damage convergence and result in the parameter estimate that reflect data errors rather than system description. Phase 3 data are known to be prone to data errors from non-compliance to incorrect dosing to sample mixed up, you name it. On the other hand, it is not feasible to check several hundred records (say 300-400 patients with 3-4 samples each = 1-2K records) manually/visually. In this situation it could be a valid strategy to exclude 20-30 (say, 1-2-3%) of records based on some diagnostic. WRES is one of the indicators of the mis-fit; large WRES are indicative of outliers/possible data errors. If you do not like WRES, you may try CWRES or any other diagnostic. Often you see the problem immediately: large cloud of reasonable WRES and several points far up or down the scale. Those few suspicious observations can be manually checked for problems: more often that not you may see that the observation is likely to be an error (much higher than the previous one without extra dose, or nearly zero followed by something reasonable, etc.). If so, decision to exclude can be backed by WRES as diagnostic + explanation based on the plots. Leonid