NONMEM Analysis for Future Bayesian Estimation

I'm trying to work through the cycle of analyzing clinical data (as for an aminoglycoside) using a one compartment model with elimination rate described by: kel = a * CR-CL + b and V with the intent of being able to use the results for Bayesian analysis of future data. My understanding is that I would like to calculate population mean and standard deviation values for 'a', 'b', and V. Thus I simulated 256 data sets (one dose and two data points per set) and analyzed the combined data using NONMEM. Model: One compartment model with a one hour infusion. 256 data set generated One time point between 1.25 and 2.5 and one time point between 6 and 12 hours. Model parameters used for the simulation CR-Cl = 65 +/- 20 (std.dev.) between 5 - 125 kel = a*CR-Cl + b a = 0.005 +/- 10 % (c.v.) between 0.0001 - 0.1 b = 0.01 +/- 20 % (c.v.) between 0.0001 - 1.0 V = 15 +/- 5 % (c.v) between 1 - 200 Data for first 5 subjects... 1 90 90 0.0 0.0 69.16 1 0.0 0.0 1.75 3.44664 69.16 1 0.0 0.0 9.00 0.27351 69.16 2 60 60 0.0 0.0 67.25 2 0.0 0.0 2.00 2.64588 67.25 2 0.0 0.0 8.00 0.35496 67.25 3 60 60 0.0 0.0 113.72 3 0.0 0.0 2.50 1.34226 113.72 3 0.0 0.0 8.00 0.06132 113.72 4 50 50 0.0 0.0 74.7 4 0.0 0.0 1.75 1.78155 74.7 4 0.0 0.0 9.00 0.05505 74.7 5 60 60 0.0 0.0 22.85 5 0.0 0.0 2.00 3.2508 22.85 5 0.0 0.0 6.00 2.38014 22.85 ...... My first attempt was to include ETA's for both the 'a' and 'b' values. Thus after analysis I could include these in the Bayesian analysis. The control file and the (some) output are below: NONMEM control file > nmtran > nonmem $PROBLEM Aminoglycoside example $INPUT ID AMT RATE TIME DV CRCL $DATA TEST $SUBROUTINES ADVAN1 $PK TA = THETA(1)*(1.+ETA(1)) TB = THETA(2)*(1.+ETA(2)) K = TA*CRCL + TB V = THETA(3)*(1.+ETA(3)) S1 = V $ERROR Y = F*(1. + ERR(1)) $THETA (0.,.005,0.1) (0.,.01,1.0) (1,15,100) $OMEGA .2 .2 .2 $SIGMA .2 $ESTIMATION PRINT=5 MAXEVALS=900 $COVARIANCE $TABLE ID TIME AMT DV $SCAT PRED VS DV UNIT $SCAT WRES VS DV TIME CRCL Partial output from NONMEM *** MINIMUM VALUE OF OBJECTIVE FUNCTION *** -1266.431 *** FINAL PARAMETER ESTIMATE THETA - VECTOR OF FIXED EFFECTS ********************* TH 1 TH 2 TH 3 4.93E-03 8.75E-03 1.50E+01 OMEGA - COV MATRIX FOR RANDOM EFFECTS - ETAS ******** ETA1 ETA2 ETA3 ETA1 9.29E-03 ETA2 0.00E+00 1.54E-08 ETA3 0.00E+00 0.00E+00 3.02E-03 SIGMA - COV MATRIX FOR RANDOM EFFECTS - EPSILONS **** EPS1 EPS1 5.06E-03 This gives: Theta(1) = a = 4.93E-03 +/- 9.64 % sqrt(9.29E-03) * 100 Theta(2) = b = 8.75E-03 +/- 0.0124 % sqrt(1.54E-08) * 100 Theta(3) = V = 15.0 +/- 5.50 % sqrt(3.02E-03) * 100 Data = +/- 7.11 % sqrt(5.06E-03) * 100 These are all similar to the input parameters (except the c.v. for 'b'). Modeling these data as apparently suggested in the manual etc., I used the control file below: $PROBLEM Aminoglycoside example $INPUT ID AMT RATE TIME DV CRCL $DATA TEST $SUBROUTINES ADVAN1 $PK TA = THETA(1) TB = THETA(2) TK = TA*CRCL + TB K = TK*(1.+ETA(1)) V = THETA(3)*(1.+ETA(2)) S1 = V $ERROR Y = F*(1. + ERR(1)) $THETA (0.,.005,0.1) (0.,.01,1.0) (1,15,100) $OMEGA .2 .2 $SIGMA .2 $ESTIMATION PRINT=5 MAXEVALS=900 $COVARIANCE $TABLE ID TIME AMT DV $SCAT PRED VS DV UNIT $SCAT WRES VS DV TIME CRCL This produced the results: *** MINIMUM VALUE OF OBJECTIVE FUNCTION *** -1265.415 *** FINAL PARAMETER ESTIMATE THETA - VECTOR OF FIXED EFFECTS ********************* TH 1 TH 2 TH 3 4.94E-03 8.04E-03 1.50E+01 OMEGA - COV MATRIX FOR RANDOM EFFECTS - ETAS ******** ETA1 ETA2 ETA1 8.82E-03 ETA2 0.00E+00 3.02E-03 SIGMA - COV MATRIX FOR RANDOM EFFECTS - EPSILONS **** EPS1 EPS1 5.05E-03 This gives: Theta(1) = a = 4.94E-03 Theta(2) = b = 8.04E-03 kel = a * CR-CL + b +/- 9.39 % sqrt(8.82E-03) * 100 Theta(3) = V = 1.50E+01 +/- 5.50 % sqrt(3.02E-03) * 100 Data = +/- 7.11 % sqrt(5.06E-03) * 100 These are all similar to the input parameters (except for the meaning of the kel c.v.). This seemed to be a satisfactory approach except for the input into the subsequent Bayesian analysis. Any comments or suggestions. Thanks.