Hi Hanna, I did not check the whole model code, but could it be a typo in the rate for re-distribution that produces the difference?
DADT(3) = K23*A(2) - K23*A(3) Kind regards Jakob Jakob Ribbing, Ph.D. Senior Consultant, Pharmetheus AB Cell/Mobile: +46 (0)70 514 33 77 jakob.ribb...@pharmetheus.com www.pharmetheus.com Phone, Office: +46 (0)18 513 328 Uppsala Science Park, Dag Hammarskjölds väg 52B SE-752 37 Uppsala, Sweden This communication is confidential and is only intended for the use of the individual or entity to which it is directed. It may contain information that is privileged and exempt from disclosure under applicable law. If you are not the intended recipient please notify us immediately. Please do not copy it or disclose its contents to any other person. On 12 Dec 2016, at 10:13, Silber Baumann, Hanna <hanna.silber_baum...@roche.com> wrote: > Dear nmusers, > I have a data set which contains single and multiple ascending dose data. The > model development was initially performed on the single dose data. > I initially developed a model using ADVAN4 TRANS 2 (2 compartment linear > model with oral administration) which I later reparameterized into ADVAN6. I > expected to see some minor differences in parameter estimates, OFV etc due to > the change in subroutine but was surprised to see large differences in both > parameter estimates and OFV (+180 points) but also a significant improvement > in overall fit (graphically) while the data was the same. With the ADVAN4 the > model fit was particularly poor to parts of the multiple dose data, with the > ADVAN6 the overall fit to all data was much improved. I was using NONMEM7.3 > for the analysis. > > I guess the ADVAN4 model gets stuck in a local minima, but using the final > estimates from the ADVAN6 model does not help. I would be grateful for an > explanation of the reasons why this happens. > > I have included the two models below. > Kind regards, > Hanna Silber > > $PROBLEM PK with ADVAN4 > > $INPUT C ID TAD TIME AMT DV EVID CMT PTIM LDV DOSE BW BMI CLCR SEX AGE > STUDY DAY BLQ > > $DATA nmpk05DEC16.csv IGNORE=@ > > $SUBROUTINES ADVAN4 TRANS4 > > $PK > CL = THETA(1) * EXP(ETA(1)) > V2 = THETA(2) * EXP(ETA(2)) > KA = THETA(3) * EXP(ETA(3)) > ALAG1 = THETA(6) * EXP(ETA(4)) > Q = THETA(7) * EXP(ETA(5)) > V3 = THETA(8) * EXP(ETA(6)) > > S2 = V2/1000 > > $ERROR > IPRED = F > W = SQRT(THETA(4)**2*IPRED**2 + THETA(5)**2) > Y = IPRED + W*EPS(1) > IRES = DV-IPRED > IWRES = IRES/W > > $THETA > (0,12.7) ;1 CL > (0,275) ;2 V2 > (0,3.06) ;3 KA > (0, 0.12) ;4 Prop.RE (sd) > (0, 0.0153) ;5 Add.RE (sd) > (0,0.474) ;6 ALAG1 > (0,26.3) ;7 Q > (0,133) ;8 V3 > > $OMEGA BLOCK(2) 0.0747 ;1 IIV CL > 0.0723 0.0942 ;2 IIV V2 > $OMEGA > 1.76 ;3 IIV KA > 0.00166 ;4 IIV ALAG > 0.036 ;5 IIV Q > 0.0407 ;6 IIV V3 > > $SIGMA > 1 FIX ; > > $EST METHOD=1 INTER MAXEVAL=9999 NOABORT SIG=3 PRINT=1 POSTHOC > $COV > ###################################################### > > $PROBLEM PK with ADVAN6 > > $INPUT C ID TAD TIME AMT DV EVID CMT PTIM LDV DOSE BW BMI CLCR SEX AGE > STUDY DAY BLQ > > $DATA nmpk05DEC16.csv IGNORE=@ > > $SUBROUTINES ADVAN6 TOL=5 > > $MODEL > COMP = (ABS) ;1 > COMP = (CENT) ;2 > COMP = (PER) ;3 > > $PK > CL = THETA(1) * EXP(ETA(1)) > V2 = THETA(2) * EXP(ETA(2)) > KA = THETA(3) * EXP(ETA(3)) > ALAG1 = THETA(6) * EXP(ETA(4)) > Q = THETA(7) * EXP(ETA(5)) > V3 = THETA(8) * EXP(ETA(6)) > > K=CL/V2 > K23 = Q/V2 > K32 = Q/V3 > > A_0(1) = 0 > A_0(2) = 0 > A_0(3) = 0 > > $DES > DADT(1) = -KA*A(1) > DADT(2) = KA*A(1) - K*A(2) - K23*A(2) + K32*A(3) > DADT(3) = K23*A(2) - K23*A(3) > > $ERROR > CONC = A(2)*1000/V2 > IPRED = CONC > IF(CONC.EQ.0) IPRED = 1 > > W = SQRT(THETA(4)**2*IPRED**2 + THETA(5)**2) > Y = IPRED + W*EPS(1) > IRES = DV-IPRED > IWRES = IRES/W > > $THETA > (0,12.1) ;1 CL > (0,275) ;2 V2 > (0,3.06) ;3 KA > (0, 0.12) ;4 Prop.RE (sd) > (0, 0.0153) ;5 Add.RE (sd) > (0,0.474) ;6 ALAG1 > (0,26.3) ;7 Q > (0,133) ;8 V3 > > $OMEGA BLOCK(2) 0.0747 ;1 IIV CL > 0.0723 0.0942 ;2 IIV V2 > $OMEGA > 1.76 ;3 IIV KA > 0.00166 ;4 IIV ALAG > 0.036 ;5 IIV Q > 0.0407 ;6 IIV V3 > > $SIGMA > 1 FIX ; > > $EST METHOD=1 INTER MAXEVAL=9999 NOABORT SIG=3 PRINT=1 POSTHOC > $COV > > ############################### > Data set example: > C ID TAD TIME AMT DV EVID CMT PTIM LDV > DOSE BW BMI CLCR SEX AGE STUDY DAY BLQ > 0 11001 0 0 5 0 1 1 0 0 > 5 54.8 20.63 74.32657 0 44 1 1 0 > 0 11001 0.5 0.5 0 1.94 0 2 0.5 > 0.662688 5 54.8 20.63 74.32657 0 44 1 > 1 0 > 0 11001 1 1 0 14.6 0 2 1 > 2.681022 5 54.8 20.63 74.32657 0 44 1 > 1 0 > 0 11001 1.5 1.5 0 22.4 0 2 1.5 > 3.109061 5 54.8 20.63 74.32657 0 44 1 > 1 0 > 0 11001 2 2 0 18.1 0 2 2 > 2.895912 5 54.8 20.63 74.32657 0 44 1 > 1 0 > 0 11001 2.5 2.5 0 15.4 0 2 2.5 > 2.734368 5 54.8 20.63 74.32657 0 44 1 > 1 0 > 0 11001 3 3 0 16.3 0 2 3 > 2.791165 5 54.8 20.63 74.32657 0 44 1 > 1 0 > 0 11001 4 4 0 15.5 0 2 4 2.74084 > 5 54.8 20.63 74.32657 0 44 1 1 0 > 0 11001 6 6 0 11.9 0 2 6 > 2.476538 5 54.8 20.63 74.32657 0 44 1 > 1 0 > 0 11001 8 8 0 11.5 0 2 8 > 2.442347 5 54.8 20.63 74.32657 0 44 1 > 1 0 > 0 11001 12 12 0 7.71 0 2 12 > 2.042518 5 54.8 20.63 74.32657 0 44 1 > 1 0 > 0 11001 16.017 16.017 0 8.71 0 2 16 > 2.164472 5 54.8 20.63 74.32657 0 44 1 > 2 0 > 0 11001 24 24 0 5.55 0 2 24 > 1.713798 5 54.8 20.63 74.32657 0 44 1 > 2 0 > 0 11001 48 48 0 3.5 0 2 48 > 1.252763 5 54.8 20.63 74.32657 0 44 1 > 3 0 > 0 11001 72 72 0 1.86 0 2 72 > 0.620576 5 54.8 20.63 74.32657 0 44 1 > 4 0 > 0 11001 120.883 120.883 0 0.597 0 2 120 > -0.51584 5 54.8 20.63 74.32657 0 44 1 > 6 0 > 0 11001 144.9 144.9 0 0.356 0 2 144 > -1.03282 5 54.8 20.63 74.32657 0 44 1 > 7 0 > 0 11001 168.883 168.883 0 0.177 0 2 168 > -1.73161 5 54.8 20.63 74.32657 0 44 1 > 8 0 > > > > -- >
