Dear Paul, I don't think you should expect the same ETA for CL under the two mixtures, but estimate two separate ones as shown below.. Note also that the estimate of ETA you get in the table file is the one from the most probable mixture component (whereas the contribution from both mixture components for each subject contributes to the likelihood). To know which the most probably mixture is for each subject output EST after stating EST=MIXEST.
I would change the estimation model from CL=(Z*(CL1 + CLr) + (1.0-Z)*(CL2 + CLr))* EXP(ETA(1)) V2 = THETA(4)*(WT/70)*EXP(ETA(2)) To CL=Z*(CL1 + CLr)* EXP(ETA(1)) CL=(1.0-Z)*(CL2 + CLr)* EXP(ETA(2)) V2 = THETA(4)*(WT/70)*EXP(ETA(3)) (I'm not sure about your ETA variance structure as it is not entirely provided, but if you use a covariance between CL and V use also separate ETAs for V between mixtures) Best regards, Mats Mats Karlsson, PhD Professor of Pharmacometrics Dept of Pharmaceutical Biosciences Faculty of Pharmacy Uppsala University Box 591 75124 Uppsala Phone: +46 18 4714105 Fax + 46 18 4714003 -----Original Message----- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Paul Hutson Sent: 07 May 2013 17:32 To: nmusers@globomaxnm.com Subject: [NMusers] Mixture model simulation Dear Users: I note the Jan 26, 2013 response to Nick Holford's query about results from the use of the $MIX mixture model for simulation. I have created a data set of N=100 subjects using R to randomly distribute their covariates, both continuous and categorical. I then ran the following sim with SUBPOP=1 to generate their corresponding DV values using the following code: ; SIMULATION CTL $PROBLEM SIM 2COMP $INPUT ID TIME AMT DV WT HT BMI BSA GFR AGE SEX TOB EVID $DATA MethodSim1.CSV IGNORE=# $SUBROUTINES ADVAN4 TRANS4 $SIMULATION (12345) SUBPROBLEMS=1 ONLYSIMULATION $MIX NSPOP=2 P(1)=THETA(7) P(2)=1.0-THETA(7) $PK KA=THETA(1)* EXP(ETA(1)); ETA removed in subsequent fitting of data CL1=THETA(2)*((WT/70)**0.75) ; non-renal clearance of subpop1 CL2=THETA(3)*((WT/70)**0.75); non-renal clearance of subpop1 CLr=(GFR*60/1000)*0.5 ; renal clearance Z=1 IF(MIXNUM.EQ.2) Z=0 CL=(Z*(CL1 + CLr) + (1.0-Z)*(CL2 + CLr))* EXP(ETA(2)) V2 = THETA(4)*(WT/70)*EXP(ETA(3)) Q = THETA(5)*(WT/70)**0.75 V3 =THETA(6)*(WT/70) S2=V2 $ERROR IPRE = F W1=F DEL = 0 IF(IPRE.LT.0.001) DEL = 1 IRES = DV-IPRE; NEGATIVE TREND IS OVERESTIMATING IPRED WRT DV IWRE = IRES/(W1+DEL) Y=F*(1+ERR(1)) $THETA (2); KAS $THETA (0.1); CL1 $THETA (5); CL2 $THETA (5); VC $THETA (12); Q $THETA (40); VP $THETA (0.4); FZ $OMEGA 0 FIXED; IEKA $OMEGA 0 FIXED; IECL $OMEGA 0 FIXED; IEV2 $SIGMA 0.03; $TABLE ID TIME AMT DV WT HT BMI BSA GFR AGE SEX TOB EVID NOPRINT NOHEADER NOAPPEND FILE=SimData.txt However, when I come back and attempt to model the simulated data set, my ETA1 on CL (note difference from the simulation ctl above) still shows a bimodal distribution. With the incorporation of the $MIXture model , I would expect a unimodal distribution of ETA_CL entered on 0. Can the community please advise? ;FITTED CTL $MIX NSPOP=2 P(1)=THETA(7) P(2)=1.0-THETA(7) $PK KA=THETA(1) CL1=THETA(2)*((WT/70)**0.75) CL2=THETA(3)*((WT/70)**0.75) RS=THETA(8) CLr=(GFR*60/1000)*RS Z=1 IF(MIXNUM.EQ.2) Z=0 CL=((Z*CL1 + CLr) + ((1.0-Z)*CL2 + CLr))*EXP(ETA(1)) V2 = THETA(4)*(WT/70)*EXP(ETA(2) Q = THETA(5)*(WT/70)**0.75 V3 =THETA(6)*(WT/70) Thanks Paul -- Paul R. Hutson, Pharm.D. Associate Professor UW School of Pharmacy T: 608.263.2496 F: 608.265.5421
