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RE: Question about handling BLOQ data with mixture model

From: Martin Bergstrand <martin.bergstrand>
Date: Sat, 26 May 2012 14:05:31 +0700

Dear Andy and Yaping,

 

I am sorry for my embarrassingly bad advice. I can now see that it is my
clumsy code that does not sum up to 1. That I should have written and how
normally code it is this:

 

$MIX

  NSPOP=3

  P(1) = THETA(8)/100

  P(2) = (1-THETA(8)/100)*THETA(9)/100

  P(3) = 1-THETA(8)/100 -((1-THETA(8)/100)*THETA(9)/100)

 

$THETA (0, 3.78, 100) ; PMIX1

$THETA (80, 91, 100) ; PMIX2/(1-PMIX1)

 

This is as the sober me can see mathematically identical to what you did. I
should get an alcohol lock for outlook so that I can't do NMusers postings
after more than 1 beer.

 

For what it is worth coming from me you could try to add a very small number
to the CUMD (e.g. 10^-6). If you have a BQL observation that NONMEM predicts
a very low probability NONMEM will in my experience crash. When you get a
run working with this arbitrary imputation you can investigate what
observations that has this extremely low probability and investigate if
there seem to be something fishy about them. In general it can be said that
the Laplacian estimation method in NONMEM is notoriously instable and
slightly prone to local minima. It can for that reason be useful to by
default try running with perturbed initial estimates.

 

Good luck,

Martin

 

From: owner-nmusers
Behalf Of Andy Stein
Sent: den 26 maj 2012 01:45
To: LGibiansky
Cc: nmusers
Subject: Re: [NMusers] Question about handling BLOQ data with mixture model

 

I wanted to follow up on the comments to Yaping's email. First, the three
probabilities below from the original code do in fact sum to 1.

 

   P(1)=THETA(8)/100

   P(2)=(1-THETA(8)/100)*THETA(9)/1000

   P(3)=(1-THETA(8)/100)*(1-THETA(9)/1000)

 

Note that: P(2) + P(3) = 1-THETA(8)/100

 

And thus P(1) + P(2) + P(3) = 1

 

Also, the model worked completely fine when the BLOQ part of the code was
left out and only the mixture was modeled. That is what led us to think
that the combination of BLOQ with a Mixture was causing the problem.

 

Andy

 

On Fri, May 25, 2012 at 1:07 PM, LGibiansky
<LGibiansky

Sum of probabilities should sum to 1. More standard way would be to use

P(1) = 1/(1+THETA(8)+THETA(9))

P(2) = THETA(8)/(1+THETA(8)+THETA(9))

P(3) = THETA(9)/(1+THETA(8)+THETA(9))

where THETA(8) and THETA(9) are any positive numbers
Regards
Leonid

Original Message:
-----------------
From: Martin Bergstrand martin.bergstrand
Date: Fri, 25 May 2012 22:59:45 +0700
To: yapingz2011
Subject: RE: [NMusers] Question about handling BLOQ data with mixture model



Dear Yaping,



I can see that you need to make any particular consideration because you
are applying a mixture model. CUMD is dependent on IPRED that in its turn is
dependent on the assigned mixture. That should be enough.



However, I spot what must be an error in your way of defining your mixture
probabilities. As it is now you total probability does not sum up to 1. Why
don't you parameterize it his way:



$MIX

  NSPOP=3

  P(1) = THETA(8)/100

  P(2) = (1-THETA(8)/100)*THETA(9)/100

  P(3) = 1-THETA(8)/100 -THETA(9)/100



$THETA (0, 3.78, 100) ; PMIX1

$THETA (80, 91, 100) ; PMIX2/(1-PMIX1)



I have kept the division of THETAs by 100 since I assume that you want
estimates in %. However the division with 1000 did not make any sense to me
despite the correctly assigned THETA boundaries?



Finally a word of caution, be careful with the use of NONMEM reserved
variables such as T (integrated time in $DES) and F (default model
prediction with some ADVANS). In my experience things can go wrong when you
use them outside the way it was intended, conflicts can occur.



Kind regards,



Martin Bergstrand, PhD

Pharmacometrics Research Group

Dept of Pharmaceutical Biosciences

Uppsala University

Sweden

martin.bergstrand



Visiting scientist:

Mahidol-Oxford Tropical Medicine Research Unit,

Bangkok, Thailand





From: owner-nmusers
Behalf Of Yaping Zhang
Sent: den 25 maj 2012 02:04
To: nmusers
Subject: [NMusers] Question about handling BLOQ data with mixture model



Hello NMUsers,



I am trying to analyze BLOQ data using the M3 method (Stuart Beal, Ways to
Fit a PK Model with Some Data Below the Quantification Limit, 2001). The
model I have is a mixture model to describe PD response of three
subpopulations. The complete control stream is pasted below.



I have implemented just the mixture model (no BLOQ) and just the BLOQ error
model (no mixture) and it works fine with nonmem 6. But the run crashed
immediately using nonmem 6 if including both BLOQ and the mixture model.



I am wondering if I need to account for the mixture in the section of the
code below when putting a mixture model together with the BLOQ error model

IF (BLOQ.EQ.1) THEN

F_FLAG=1

Y =CUMD + something based on MIXNUM?

ENDIF



Any ideas are gratefully received!



Many thanks,

Yaping





$PROB AMN107A2303



$INPUT NUM=DROP STUD=DROP SUBJ=DROP ID AGE0=DROP SEX=DROP RACE=DROP

DART=DROP ARM ACTT=DROP POP PPK=DROP COUN=DROP

SOK OTIM=DROP TIME TVIS=DROP

AMT=DROP DOSE=DROP SCHD=DROP AUC=DROP CMIN=DROP

ODV=DROP MDV DV BLOQ STY=DROP EVDT=DROP



$DATA ../data/AMN2303_ENEST.csv ; currently modified with matlab and saved
with oocalc



IGNORE=

IGNORE=(MDV.EQ.1)

IGNORE=(ID.EQ.66, ID.EQ.92, ID.EQ.335, ID.EQ.346, ID.EQ.348, ID.EQ.416,
ID.EQ.496, ID.EQ.527, ID.EQ.762, ID.EQ.790)



$PRED

mu =THETA(1)*EXP(ETA(1))

AA =THETA(2)*EXP(ETA(2))

alpha =THETA(3)*EXP(ETA(3))

BB =THETA(4)*EXP(ETA(4))

beta =THETA(5)*EXP(ETA(5))

InCC =THETA(6)+ETA(6)

gamma =THETA(7)*EXP(ETA(7))



T = TIME

IF (TIME.LE.0) T = 0



EST=MIXEST

IF (MIXNUM.EQ.1) F =AA*EXP(mu*T/8766)

IF (MIXNUM.EQ.2) F =AA*EXP(alpha*T/8766)+BB*EXP(beta*T/8766)

IF (MIXNUM.EQ.3) F
=AA*EXP(alpha*T/8766)+BB*EXP(beta*T/8766)+EXP(InCC)*EXP(gamma*T/8766)



PROP=THETA(10)

W=SQRT(PROP*PROP)



IPRED=-2.8

IF(F.GT.0)IPRED =LOG10(F)

LLOQ=-2.5

DUM=(LLOQ-IPRED)/W

CUMD=PHI(DUM)



IF (BLOQ.EQ.0) THEN

F_FLAG=0

Y =IPRED+W*ERR(1)

ENDIF



IF (BLOQ.EQ.1) THEN

F_FLAG=1

Y =CUMD

ENDIF



$MIX

  NSPOP=3

  P(1)=THETA(8)/100

  P(2)=(1-THETA(8)/100)*THETA(9)/1000

  P(3)=(1-THETA(8)/100)*(1-THETA(9)/1000)



$THETA (-10,-0.57,0) ; mu THETA(1)

$THETA (0.0001,50.7,100) ; AA THETA(2)

$THETA (-100,-14.2,0) ; alpha THETA(3)

$THETA (0.0001,0.196,10) ; BB THETA(4)

$THETA (-10,-0.678,0) ; beta THETA(5)

$THETA (-100,-6.92,0) ; InCC THETA(6)

$THETA (0, 2.15) ; gamma THETA(7)

$THETA (0, 3.78, 100) ; THETA(8)

$THETA (800, 910, 1000) ; THETA(9)

$THETA (0.001,0.104, 5) ; ERR THETA(10)



$OMEGA 0.001 FIX ; mu

$OMEGA 0.767 ; AA

$OMEGA 0.236 ; alpha

$OMEGA 3.58 ; BB

$OMEGA 0.148 ; beta

$OMEGA 5 ; CC

$OMEGA 0.858 ; gamma



$SIGMA 1 FIX



$EST METHOD=1 LAPLACIAN NOABORT MAXEVAL=9990 PRINT=1 MSFO=run012.nmmsf SIG=2



$COV



$TABLE ID TIME DV MDV IPRED EST

FILE=run012.nmfit NOPRINT ONEHEADER



$TABLE ID ETA1 ETA2 ETA3 ETA4 ETA5 ETA6 ETA7 mu AA alpha BB beta InCC gamma

NOAPPEND FIRSTONLY FILE=run012.nmpar NOPRINT ONEHEADER





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Received on Sat May 26 2012 - 03:05:31 EDT

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