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=#

$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

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

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=#

$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

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