# RE: Mixture model simulation

From: E.Olofsen
Date: Tue, 7 May 2013 18:48:22 +0000

Dear Paul,

All OMEGAs are zero during simulation? So I'm thinking about what that woul=
d mean for ETA_CL if is not fixed to zero when fitting; what would happen t=
o ETA_CL if not all estimated subgroups are equal to the simulated ones, or=
the effect of a less than perfect fit on ETA_CL might be different for the=
subgroups?

Erik
________________________________________
From: owner-nmusers
of Paul Hutson [prhutson
Sent: Tuesday, May 07, 2013 5:31 PM
To: nmusers
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=#
\$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

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.

;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
Received on Tue May 07 2013 - 14:48:22 EDT

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