From: Leonid Gibiansky <*LGibiansky*>

Date: Wed, 22 Oct 2008 17:46:41 -0400

Hi Bill,

I think, description in $PRIOR and nwpri help entries are the most

helpful. The simplest 1-compartment problem example is below.

Good luck!

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

----------

$PROBLEM 001, example

$INPUT ID,TIME,AMT,DV,MDV,EVID

$DATA data.csv

$SUBROUTINES ADVAN1 TRANS1

$PRIOR NWPRI NTHETA=3 NETA=2 NEPS=1 NTHP=3 NETP=2 NPEXP=1

$PK

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

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

K=CL/V

SD1 = THETA(3)

S1=V

$ERROR

Y=A(1)/V*EXP(SD1*EPS(1))

$THETA ; INITIAL ESTIMATES FOR THETAS

(0,1) ; 1 CL

(0,1) ; 2 V

(0,0.3) ; 3 error SD

$THETA ; PRIOR MEAN OF THETAS

2 FIXED ; 1 CL

2 FIXED ; 2 V

0.3 FIXED ; 3 error SD

$THETA ; degrees of freedom for OMEGAs

20 FIXED ; 1 CL

20 FIXED ; 2 V1

$OMEGA ; current-problem omegas

0.1 ;[P] 1 V1

0.1 ;[P] 2 CL

$OMEGA ; variances of the distribution for thetas

1.5 FIXED ;[P] 1 CL

1.5 FIXED ;[P] 2 V

0.1 FIXED ;[P] 3 error SD

$OMEGA ; mode of priors for omegas

0.15 FIXED ; 1 CL

0.15 FIXED ; 2 V1

$SIGMA

1 FIXED ;[P]

$EST MAXEVAL=9999 NOABORT METHOD=1

----------

data.csv:

1,0,1000,0,1,1

1,0.1,0,1000,0,0

1,0.2,0,900,0,0

1,0.3,0,800,0,0

1,0.4,0,600,0,0

1,0.5,0,500,0,0

1,1,0,200,0,0

1,2,0,100,0,0

1,3,0,50,0,0

1,4,0,25,0,0

1,5,0,10,0,0

2,0,1000,0,1,1

2,0.1,0,1000,0,0

2,0.2,0,900,0,0

2,0.3,0,800,0,0

2,0.4,0,600,0,0

2,0.5,0,500,0,0

2,1,0,200,0,0

2,2,0,100,0,0

2,3,0,50,0,0

2,4,0,25,0,0

2,5,0,10,0,0

Denney, William S. wrote:

*> Hi Leonid,
*

*>
*

*> It is not obvious to me how to make use of the next level of random
*

*> effects using the PRIOR subroutine in the html help. Can you point me
*

*> to an example or other documentation of how to use PRIOR for this?
*

*>
*

*> Thanks,
*

*>
*

*> Bill
*

*>
*

*> -----Original Message-----
*

*> From: owner-nmusers *

*> On Behalf Of Leonid Gibiansky
*

*> Sent: Friday, October 17, 2008 10:35 AM
*

*> To: Michael.J.Fossler *

*> Cc: nmusers; Nick Holford
*

*> Subject: Re: [NMusers] More Levels of Random Effects
*

*>
*

*> The next level of random effects can be introduced in NONMEM using PRIOR
*

*>
*

*> subroutine. It existed as undocumented feature even in Nonmem V. Now (in
*

*>
*

*> Nonmem VI) it is official (see help).
*

*> Leonid
*

*>
*

*> --------------------------------------
*

*> Leonid Gibiansky, Ph.D.
*

*> President, QuantPharm LLC
*

*> web: www.quantpharm.com
*

*> e-mail: LGibiansky at quantpharm.com
*

*> tel: (301) 767 5566
*

*>
*

*>
*

*>
*

*>
*

*> Michael.J.Fossler *

*>>
*

*>> I suppose it really comes down to what you are going to do with the
*

*>> model. Many times I have checked the SAME assumption when modeling
*

*>> inter-occasional variability, and found that sometimes, removing it
*

*> does
*

*>> indeed improve the fit significantly. In almost every case I've
*

*>> retained it (despite the better fit) for the exact reasons Leonid
*

*> cites:
*

*>> it makes your model completely data-dependent. I suppose if the model
*

*>> was meant as a description or summary of the data, then it would not
*

*>> matter, but I make all of my models work for a living...
*

*>>
*

*>> There is a related topic which I'd be interested in hearing from the
*

*>> group about. Many times, we take several Phase 1 studies and put them
*

*>> together in order to develop a population model early in development.
*

*>> I've learned through experience to be careful when doing this, because
*

*>
*

*>> often, one or more studies will appear to have a different mean
*

*> response
*

*>> for some parameter, e.g., CL or V2. Of course, you can introduce study
*

*>
*

*>> as a covariate, but this intrduces the same problem as above; in a
*

*>> simulation context, which CL value is correct? There is a work-around
*

*>> for this (use both values) but this doubles the number of simulations
*

*>> you have to do, and from a scientific stand-point it is not very
*

*>> satisfying. What we need is another level of random effects at the
*

*> STUDY
*

*>> level, similar to what is routinely done when performing hierarchical
*

*>> modeling in something like WinBUGS. I'd love to see this feature in a
*

*>> future version of NONMEM.
*

*>>
*

*>>
*

*>>
*

*>>
*

*>>
*

*>>
*

*>> *"Leonid Gibiansky" <LGibiansky *

*>> Sent by: owner-nmusers *

*>>
*

*>> 17-Oct-2008 09:30
*

*>>
*

*>>
*

*>> To
*

*>> "Nick Holford" <n.holford *

*>> cc
*

*>> "nmusers" <nmusers *

*>> Subject
*

*>> Re: [NMusers] More Levels of Random Effects
*

*>>
*

*>>
*

*>>
*

*>>
*

*>>
*

*>>
*

*>>
*

*>>
*

*>> Nick,
*

*>>
*

*>> This is exactly what I meant. If you have a model for English, Irish
*

*> and
*

*>> Welsh, you may at least extrapolate it to Australians and New
*

*> Zealanders
*

*>> (of British descent :) ). With occasion treated as non-ordered
*

*>> categorical covariate, you cannot extrapolate the model at all because
*

*>> time cannot be repeated, so your covariate (occasion) will have
*

*>> different value (level) at any future trial.
*

*>>
*

*>> Leonid
*

*>>
*

*>> --------------------------------------
*

*>> Leonid Gibiansky, Ph.D.
*

*>> President, QuantPharm LLC
*

*>> web: www.quantpharm.com
*

*>> e-mail: LGibiansky at quantpharm.com
*

*>> tel: (301) 767 5566
*

*>>
*

*>>
*

*>>
*

*>>
*

*>> Nick Holford wrote:
*

*>> > Leonid,
*

*>> >
*

*>> > I dont understand what you mean by "we lose predictive power of the
*

*>> > model: we do not know what will be
*

*>> > the variability on the next occasion.".
*

*>> >
*

*>> > Or are you concerned about the situation where you have say 3
*

*> occasions
*

*>> > and the IOV seems to be different on each occasion but you now want
*

*> to
*

*>> > predict the IOV for a future study on the 4th occasion?
*

*>> >
*

*>> > I agree it is hard to extrapolate to future occasions but this
*

*> seems to
*

*>> > be just like any other non-ordered categorical covariate - e.g. if
*

*> we
*

*>> > see differences between English, Irish and Welsh what difference
*

*> would
*

*>> > you expect for Russians? :-)
*

*>> >
*

*>> > Nick
*

*>> >
*

*>> >
*

*>> > Leonid Gibiansky wrote:
*

*>> >> Hi Xia, Nick
*

*>> >> Technically, one can use different variances on different
*

*> occasions but
*

*>> >> then we loose predictive power of the model: we do not know what
*

*> will be
*

*>> >> the variability on the next occasion. One can use
*

*> occasion-dependent IOV
*

*>> >> variance to check for trends (for example, to investigate the time
*

*>> >> dependence of the IOV variability, or to check whether the first
*

*>> >> occasion (e.g., after the first dose of a long-term study) is for
*

*> some
*

*>> >> reasons different from the others) but the final model should have
*

*> some
*

*>> >> condition that specifies the relations of IOV variances at
*

*> different
*

*>> >> occasion (SAME being the simplest, most reasonable and the
*

*> most-often
*

*>> >> used option).
*

*>> >>
*

*>> >> Thanks
*

*>> >> Leonid
*

*>> >>
*

*>> >> --------------------------------------
*

*>> >> Leonid Gibiansky, Ph.D.
*

*>> >> President, QuantPharm LLC
*

*>> >> web: www.quantpharm.com
*

*>> >> e-mail: LGibiansky at quantpharm.com
*

*>> >> tel: (301) 767 5566
*

*>> >>
*

*>> >>
*

*>> >>
*

*>> >>
*

*>> >> Nick Holford wrote:
*

*>> >>> Xia,
*

*>> >>>
*

*>> >>> There is no requirement to use the SAME option. However, it is a
*

*>> >>> reasonable model for IOV that it has the same variability on each
*

*>> >>> occasion.
*

*>> >>>
*

*>> >>> If you dont use the SAME option then you just need to estimate an
*

*>> >>> extra OMEGA parameter for each occasion you dont use SAME. You
*

*> can
*

*>> >>> test if the SAME assumption is supported by your data or not by
*

*>> >>> comparing models with and without SAME.
*

*>> >>>
*

*>> >>> Nick
*

*>> >>>
*

*>> >>> PS Your computer clock seems to be more than 2 years out of date.
*

*>> >>> Your email claimed it was sent in 17 Jan 2006.
*

*>> >>>
*

*>> >>> Xia Li wrote:
*

*>> >>>> Dear All,
*

*>> >>>> Do we have to assume the variability between all occasions are
*

*> the
*

*>> >>>> same when
*

*>> >>>> we estimate IOV? What will happen if I don't use the 'same'
*

*>> >>>> constrain in the
*

*>> >>>> $OMEGA BLOCK statement? Any input will be appreciated.
*

*>> >>>>
*

*>> >>>> Best,
*

*>> >>>>
*

*>> >>>> Xia Li
*

*>> >>>>
*

*>> >>>> -----Original Message-----
*

*>> >>>> From: owner-nmusers *

*>> >>>> [mailto:owner-nmusers *

*>> >>>> Behalf Of Johan Wallin
*

*>> >>>> Sent: Wednesday, October 15, 2008 9:17 AM
*

*>> >>>> To: nmusers *

*>> >>>> Subject: RE: [NMusers] More Levels of Random Effects
*

*>> >>>>
*

*>> >>>> Bill,
*

*>> >>>> Is it really an eta you want, or is this rather solved by
*

*> different
*

*>> >>>> error
*

*>> >>>> models for the different machines?
*

*>> >>>>
*

*>> >>>> If still want etas, one way would be to model in the same way as
*

*>> >>>> IOV. In the
*

*>> >>>> case of intermachine-variability you would have to assume the
*

*>> >>>> variability
*

*>> >>>> between all machines are the same... Or would you rather assume
*

*>> >>>> interindividual variability is different with
*

*>> >>>> different machine, and you then would want one eta for TH(X) for
*

*> every
*

*>> >>>> machine...? It depends on what you mean by different slope every
*

*> day,
*

*>> >>>> regarding on what your experiments like, but calibration
*

*> differences
*

*>> >>>> should
*

*>> >>>> perhaps be taken care of by looking into your error model, eta
*

*> on
*

*>> >>>> epsilon
*

*>> >>>> for starters...
*

*>> >>>>
*

*>> >>>> Without knowing your structure of data, a short example of
*

*> IOV-like
*

*>> >>>> variability would be:
*

*>> >>>>
*

*>> >>>> MA1=0
*

*>> >>>> MA2=0
*

*>> >>>> IF(MACH=1)MA1=1
*

*>> >>>> IF(MACH=2)MA2=1
*

*>> >>>> ;Intermachine variability:
*

*>> >>>> ETAM = MA1*ETA(Y)+MA2*ETA(Z)
*

*>> >>>>
*

*>> >>>> PAR= TH(X) *EXP(ETA(X)+ETAM)
*

*>> >>>>
*

*>> >>>> $OMEGA value1
*

*>> >>>> $OMEGA BLOCK(1) value2
*

*>> >>>> $OMEGA BLOCK(1) same
*

*>> >>>>
*

*>> >>>> /Johan
*

*>> >>>>
*

*>> >>>>
*

*>> >>>> _________________________________________
*

*>> >>>> Johan Wallin, M.Sci./Ph.D.-student
*

*>> >>>> Pharmacometrics Group
*

*>> >>>> Div. of Pharmacokinetics and Drug therapy
*

*>> >>>> Uppsala University
*

*>> >>>> _________________________________________
*

*>> >>>>
*

*>> >>>>
*

*>> >>>> -----Original Message-----
*

*>> >>>> From: owner-nmusers *

*>> >>>> [mailto:owner-nmusers *

*>> >>>> Behalf Of Denney, William S.
*

*>> >>>> Sent: den 15 oktober 2008 14:39
*

*>> >>>> To: nmusers *

*>> >>>> Subject: [NMusers] More Levels of Random Effects
*

*>> >>>>
*

*>> >>>> Hello,
*

*>> >>>>
*

*>> >>>> I'm trying to build a model where I need to have ETAs generated
*

*> on
*

*>> >>>> separately for the ID and another variable (MACH). What I have
*

*> is
*

*>> a PD
*

*>> >>>> experiment that was run on several different machines (MACH).
*

*> Each
*

*>> >>>> machine appears to have a different slope per day and a
*

*> different
*

*>> >>>> calibration. I still need to keep some ETAs on the ID column,
*

*> so I
*

*>> >>>> can't just assign MACH=ID.
*

*>> >>>>
*

*>> >>>> I've heard that there are ways to do similar to this, but I have
*

*> been
*

*>> >>>> unable to find examples of how to set etas to key off of
*

*> different
*

*>> >>>> columns.
*

*>> >>>>
*

*>> >>>> Thanks,
*

*>> >>>>
*

*>> >>>> Bill
*

*> Notice: This e-mail message, together with any attachments, contains
*

*> information of Merck & Co., Inc. (One Merck Drive, Whitehouse Station,
*

*> New Jersey, USA 08889), and/or its affiliates (which may be known
*

*> outside the United States as Merck Frosst, Merck Sharp & Dohme or
*

*> MSD and in Japan, as Banyu - direct contact information for affiliates is
*

*> available at http://www.merck.com/contact/contacts.html) that may be
*

*> confidential, proprietary copyrighted and/or legally privileged. It is
*

*> intended solely for the use of the individual or entity named on this
*

*> message. If you are not the intended recipient, and have received this
*

*> message in error, please notify us immediately by reply e-mail and
*

*> then delete it from your system.
*

*>
*

*> *

Received on Wed Oct 22 2008 - 17:46:41 EDT

Date: Wed, 22 Oct 2008 17:46:41 -0400

Hi Bill,

I think, description in $PRIOR and nwpri help entries are the most

helpful. The simplest 1-compartment problem example is below.

Good luck!

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

----------

$PROBLEM 001, example

$INPUT ID,TIME,AMT,DV,MDV,EVID

$DATA data.csv

$SUBROUTINES ADVAN1 TRANS1

$PRIOR NWPRI NTHETA=3 NETA=2 NEPS=1 NTHP=3 NETP=2 NPEXP=1

$PK

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

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

K=CL/V

SD1 = THETA(3)

S1=V

$ERROR

Y=A(1)/V*EXP(SD1*EPS(1))

$THETA ; INITIAL ESTIMATES FOR THETAS

(0,1) ; 1 CL

(0,1) ; 2 V

(0,0.3) ; 3 error SD

$THETA ; PRIOR MEAN OF THETAS

2 FIXED ; 1 CL

2 FIXED ; 2 V

0.3 FIXED ; 3 error SD

$THETA ; degrees of freedom for OMEGAs

20 FIXED ; 1 CL

20 FIXED ; 2 V1

$OMEGA ; current-problem omegas

0.1 ;[P] 1 V1

0.1 ;[P] 2 CL

$OMEGA ; variances of the distribution for thetas

1.5 FIXED ;[P] 1 CL

1.5 FIXED ;[P] 2 V

0.1 FIXED ;[P] 3 error SD

$OMEGA ; mode of priors for omegas

0.15 FIXED ; 1 CL

0.15 FIXED ; 2 V1

$SIGMA

1 FIXED ;[P]

$EST MAXEVAL=9999 NOABORT METHOD=1

----------

data.csv:

1,0,1000,0,1,1

1,0.1,0,1000,0,0

1,0.2,0,900,0,0

1,0.3,0,800,0,0

1,0.4,0,600,0,0

1,0.5,0,500,0,0

1,1,0,200,0,0

1,2,0,100,0,0

1,3,0,50,0,0

1,4,0,25,0,0

1,5,0,10,0,0

2,0,1000,0,1,1

2,0.1,0,1000,0,0

2,0.2,0,900,0,0

2,0.3,0,800,0,0

2,0.4,0,600,0,0

2,0.5,0,500,0,0

2,1,0,200,0,0

2,2,0,100,0,0

2,3,0,50,0,0

2,4,0,25,0,0

2,5,0,10,0,0

Denney, William S. wrote:

Received on Wed Oct 22 2008 - 17:46:41 EDT