RE: WRES AND OUTLIER IDENTIFICATION/EXCLUSION

From: Mark Sale - Next Level Solutions mark@nextlevelsolns.com Subject: RE: [NMusers] WRES AND OUTLIER IDENTIFICATION/EXCLUSION Date: Mon, 25 Sep 2006 14:38:15 -0700 I'd like to add to Bill's comment: 1. Look at a time vs dv/pred plot for that person, if you have an otherwise reasonable profile, but one strange point, I'd be more comfortable (not that CROs ever mislabel samples, or record times wrong, or mix up tubes) 2. If all samples from a person are strange, it isn't a statistical outlier. Either, that person is different, or something went wrong (wrong dose, sample put in red tube instead of green tube, samples sat on counter over the weekend, assay technician did the dilution wrong etc etc...) You can still delete them all if you want to, but you're sort of obligated to ask if this person is different and why. If we always through away data that isn't consistent with our models, we won't learn very much (think pre 2D6 genotyping studies for tricyclic antidepressants). We only learn when our models DO NOT explain the data. Sort of Learn..Confirm OR Fail to confirm..Learn, to paraphrase LBS. 3. It is very hard to justify deleting more than 3% of your data as statistical outliers. At some point you have to say that the site|lab|data management messed up. Or, this person is strange, for unknown reason, should be included in the estimate of OMEGA, and you should write a grant to study why this is happening. To answer your question about references, not that I'm aware of. But, outlier handleing should be specified in the analysis plan, then you're covered. Mark Mark Sale MD Next Level Solutions, LLC www.NextLevelSolns.com