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 11:24:33 -0400 Hi Mike, You never know whether high WRES points are outliers or bad data points: these are not labeled on the tube "outliers". I am not talking about full profile - exclusion; the discussion is whether to exclude some points on the profile that for some unknown reasons cannot be described by the model, and mainly, on how to identify those points without looking on each and every PK profile of hundreds of patients. I tried the idea of using individual sigma values. Results of the quick experiments on the recent data set is below (number of excluded data points was about 2%): High WRES excluded, same sigma THETAs OM SIGMA Value 4.05E+00 2.90E-01 1.11E-02 1.29E-02 8.06E-01 4.82E-02 9.33E-02 SE 1.20E-01 1.21E-02 1.58E-03 4.74E-03 3.41E-02 6.61E-03 8.24E-03 High WRES included, same sigma THETAs OM SIGMA Value 4.37E+00 3.08E-01 1.08E-02 1.75E-02 7.68E-01 4.72E-02 2.04E-01 SE 2.12E-01 1.09E-02 1.11E-03 5.47E-03 3.65E-02 9.27E-03 4.84E-02 High WRES excluded, individual sigma THETAs OM SIGMA OM on SIGMA Value 4.04E+00 2.90E-01 1.11E-02 1.30E-02 8.06E-01 4.82E-02 9.20E-02 2.42E-03 SE 1.18E-01 1.22E-02 1.60E-03 4.82E-03 3.43E-02 6.61E-03 9.14E-03 1.18E-02 High WRES included, individual sigma THETAs OM SIGMA OM on SIGMA Value 4.04E+00 2.93E-01 1.05E-02 1.50E-02 8.21E-01 4.65E-02 8.27E-02 2.00E-01 SE 1.19E-01 1.20E-02 1.38E-03 6.23E-03 3.66E-02 7.49E-03 1.06E-02 7.41E-02 You may see that individualizing residual error is more or less equivalent to exclusion of high WRES: weight of those points is significantly reduced by assigning high residual error to patients with those points. It is more automatic, and do not require data exclusion, great idea, Mats, thanks. Leonid