RE: WRES AND OUTLIER IDENTIFICATION/EXCLUSION

From: "Mats Karlsson" mats.karlsson@farmbio.uu.se Subject: RE: [NMusers] WRES AND OUTLIER IDENTIFICATION/EXCLUSION Date: Sat, 30 Sep 2006 17:38:50 +0200 Ken, Nice. I guess it does not come without a price as the same mixture model is applied to subjects with and without outliers alike. If one were to take this estimation route, estimating a (interindividual) mixture model for the mixing component would be a way to address this: $MIX P(1)=THETA(5) ;Proportion of subjects with outliers P(2)=1-P(1) ;Proportion of subjects without outliers $PRED MU=THETA(1)+ETA(1) MP=THETA(2) ;MP for subjects with outliers IF(MIXNUM.EQ.2) MP=0 ;MP for subjects without outliers SIG1=THETA(3) SIG2=THETA(4) IW1=(DV-MU)/SIG1 IW2=(DV-MU)/SIG2 L1=-0.5*LOG(2*3.14159265)-LOG(SIG1)-0.5*(IW1**2) L2=-0.5*LOG(2*3.14159265)-LOG(SIG2)-0.5*(IW2**2) L=(1-MP)*EXP(L1)+MP*EXP(L2) Y=-2*LOG(L) If you have really contaminated data maybe you in addition want to add a (logit-transformed) ETA on MP for subjects with outliers. 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