Predictive Performance

From: "Sarapee Hirankarn" <shirank@blue.weeg.uiowa.edu> Subject: Predictive Performance Date: Tue, 25 Apr 2000 08:42:17 -0500 Hi NM-Users, I wonder whether the procedure I tried to do as described below is appropriate or not. In order to assess the PREDICTIVE PERFORMANCE of NONMEM, I used POSTHOC Bayesian estimation method (Code below) (by this method, NONMEM will obtain individual estimates). I also deleted some of known concentrations or defined it as missing data. Then, I calculated ME (predicted concentration - actual concentration), RMSE and relative predictive error(%) from the set of missing data only. I understood that to do "population predictions or feedback predictions" as described by Stu Beal (NONMEM Topic 6) is a method of assessing the FITTING PERFORMANCE. Please let me know if I misunderstood something and if I am wrong, please also let me know how to assess PREDICTIVE PERFORMANCE of NONMEM appropriately. Thanks, Sarapee Hirankarn Graduate Student College of Pharmacy University of Iowa $PROB THEOPHYLLINE POPULATION DATA $INPUT ID DOSE=AMT TIME CP=DV WT $DATA THEOpre.txt $SUBROUTINES ADVAN2 $PK ;THETA(1)=MEAN ABSORPTION RATE CONSTANT (1/HR) ;THETA(2)=MEAN ELIMINATION RATE CONSTANT (1/HR) ;THETA(3)=SLOPE OF CLEARANCE VS WEIGHT RELATIONSHIP (LITERS/HR/KG) ;SCALING PARAMETER=VOLUME/WT SINCE DOSE IS WEIGHT-ADJUSTED CALLFL=1 KA=THETA(1)+ETA(1) K=THETA(2)+ETA(2) CL=THETA(3)*WT+ETA(3) SC=CL/K/WT $THETA (.1,3,5) (.008,.08,.5) (.004,.04,.9) $OMEGA BLOCK(3) 6 .005 .0002 .3 .006 .4 $ERROR Y=F+EPS(1) IPRED=F $SIGMA .4 $EST POSTHOC MAXEVAL=450 PRINT=5 NOABORT $COV $TABLE ID DOSE WT TIME IPRED $SCAT (RES WRES) VS TIME BY ID 1 4.02 0. . 79.6 1 . 0. .74 . 1 . 0.25 2.84 . 1 . 0.57 6.57 . 1 . 1.12 10.5 . 1 . 2.02 9.66 . 1 . 3.82 . . <- defined as missing data. 1 . 5.1 8.36 . 1 . 7.03 7.47 . 1 . 9.05 6.89 . 1 . 12.12 5.94 . 1 . 24.37 3.28 . 2 4.4 0. . 72.4 2 . 0. 0. . 2 . .27 1.72 . 2 . .52 7.91 . 2 . 1. 8.31 . 2 . 1.92 8.33 . 2 . 3.5 6.85 . 2 . 5.02 6.08 . 2 . 7.03 5.4 . 2 . 9. 4.55 . 2 . 12. . . <- defined as missing data 2 . 24.3 .90 .