From: andreas.krause

Date: Thu, 9 Jul 2009 10:05:37 +0200

Andreas,

I guess you are hinting at the difference between simulation of a large

population and simulation of a study.

The latter incorporates the added uncertainty of the parameter estimates,

as you point out.

You would simulate the population parameters with their uncertainties first

(from the "big covariance matrix" in nonmem) and then simulate the study

with these sampled population parameters (both steps many times).

Nonmem can only do the latter directly since you cannot specify the

parameter uncertainties from a previous run to be included in the

simulation.

It is fairly straightforward though since the matrix reflects a

multivariate Normal distribution.

Andreas

-----

Andreas Krause, PhD

Lead Scientist Modeling and Simulation

Actelion Pharmaceuticals Ltd / Gewerbestrasse 16 / CH-4123 Allschwil /

Switzerland

andreas.krause

-----owner-nmusers

To: <nmusers

From: andreas lindauer <lindauer

Sent by: owner-nmusers

Date: 2009-07-09 09:42

Subject: AW: [NMusers] Simulations with/without residual error

Nick,

Thank you very much for your comments.

Indeed for VPC et al. i always simulate with residual error.

I understand that when one wants to simulate the 'true' value residual

error

is not needed. But what if one wants to simulate 'real' values which will

be

observed in a future study. For example, you have a PK/PD model for an

anti-hypertensive drug and want to predict how many subjects will attain a

blood pressure below a pre-defined value. Wouldn't a simulation without

residual error result in an overoptimistic prediction because in reality

blood pressure is measured with error?

On the other hand, the estimated residual error does not only reflect

measurement error but also model misspecification etc.. So, might it be an

option to simulate not with the estimated residual error but rather with a

residual error set to the imprecision of the measurement method?

Best regards, Andreas.

.

-----Ursprüngliche Nachricht-----

Von: owner-nmusers

Auftrag von Nick Holford

Gesendet: Mittwoch, 8. Juli 2009 15:39

An: nmusers

Betreff: Re: [NMusers] Simulations with/without residual error

Andreas,

My suggestion:

If you want to compare your simulations with actual observations then

you should include residual error in the simulation. The observations

will include noise as well as the 'true' value so in order to compare

observations with simulated observations you need the residual error.

If you want to use the simulation to describe the 'true' value then dont

include the residual error. Residual error is assumed to have a mean of

zero around the 'true' value so there is no point in adding this kind of

noise if you are trying to predict the 'true' value.

Your examples suggest to me that you are trying to predict the 'true'

value -- not trying to match simulations directly with measured values.

If my guess is correct then you dont need to include residual error.

However, if you are using simulations for some kind of predictive check

(visual, numerical, statistical) that will be compared to distribution

statistics of the observations then you should include residual error.

Nick

andreas lindauer wrote:

*>
*

*> Dear NMUSERS,
*

*>
*

*>
*

*>
*

*> The recent discussion about simulation with a nonparametric method
*

*> brought a general question concerning monte-carlo simulations into my
*

*> mind. When should simulations be performed with residual error and
*

*> when not. I am especially interested in comments regarding the
*

*> following scenarios when the result of the simulation should be
*

*> reported as mean or median and 90% prediction interval:
*

*>
*

*> 1. Simulated response at a particular time point (eg. Trough values)
*

*>
*

*> 2. Simulated response at a particular time point (x) relative to
*

*> baseline response (IPRED(t=x)/IPRED(t=0) vs. DV(t=x)/DV(t=0) )
*

*>
*

*> 3. Simulated time of maximal response (eg. Tmax)
*

*>
*

*>
*

*>
*

*>
*

*>
*

*> Thanks and best regards, Andreas.
*

*>
*

*>
*

*>
*

*>
*

*>
*

*> ____________________________
*

*>
*

*>
*

*>
*

*> Andreas Lindauer
*

*>
*

*>
*

*>
*

*> Department of Clinical Pharmacy
*

*>
*

*> Institute of Pharmacy
*

*>
*

*> University of Bonn
*

*>
*

*> An der Immenburg 4
*

*>
*

*> D-53121 Bonn
*

*>
*

*>
*

*>
*

*> phone: + 49 228 73 5781
*

*>
*

*> fax: + 49 228 73 9757
*

*>
*

*>
*

*>
*

--

Nick Holford, Professor Clinical Pharmacology

Dept Pharmacology & Clinical Pharmacology

University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New

Zealand

n.holford

mobile: +33 64 271-6369 (Apr 6-Jul 20 2009)

http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford

The information of this email and in any file transmitted with it is strict=

ly confidential and may be legally privileged.

It is intended solely for the addressee. If you are not the intended recipi=

ent, any copying, distribution or any other use of this email is prohibited=

and may be unlawful. In such case, you should please notify the sender imm=

ediately and destroy this email.

The content of this email is not legally binding unless confirmed by letter.

Any views expressed in this message are those of the individual sender, exc=

ept where the message states otherwise and the sender is authorised to stat=

e them to be the views of the sender's company. For further information abo=

ut Actelion please see our website at http://www.actelion.com

Received on Thu Jul 09 2009 - 04:05:37 EDT

Date: Thu, 9 Jul 2009 10:05:37 +0200

Andreas,

I guess you are hinting at the difference between simulation of a large

population and simulation of a study.

The latter incorporates the added uncertainty of the parameter estimates,

as you point out.

You would simulate the population parameters with their uncertainties first

(from the "big covariance matrix" in nonmem) and then simulate the study

with these sampled population parameters (both steps many times).

Nonmem can only do the latter directly since you cannot specify the

parameter uncertainties from a previous run to be included in the

simulation.

It is fairly straightforward though since the matrix reflects a

multivariate Normal distribution.

Andreas

-----

Andreas Krause, PhD

Lead Scientist Modeling and Simulation

Actelion Pharmaceuticals Ltd / Gewerbestrasse 16 / CH-4123 Allschwil /

Switzerland

andreas.krause

-----owner-nmusers

To: <nmusers

From: andreas lindauer <lindauer

Sent by: owner-nmusers

Date: 2009-07-09 09:42

Subject: AW: [NMusers] Simulations with/without residual error

Nick,

Thank you very much for your comments.

Indeed for VPC et al. i always simulate with residual error.

I understand that when one wants to simulate the 'true' value residual

error

is not needed. But what if one wants to simulate 'real' values which will

be

observed in a future study. For example, you have a PK/PD model for an

anti-hypertensive drug and want to predict how many subjects will attain a

blood pressure below a pre-defined value. Wouldn't a simulation without

residual error result in an overoptimistic prediction because in reality

blood pressure is measured with error?

On the other hand, the estimated residual error does not only reflect

measurement error but also model misspecification etc.. So, might it be an

option to simulate not with the estimated residual error but rather with a

residual error set to the imprecision of the measurement method?

Best regards, Andreas.

.

-----Ursprüngliche Nachricht-----

Von: owner-nmusers

Auftrag von Nick Holford

Gesendet: Mittwoch, 8. Juli 2009 15:39

An: nmusers

Betreff: Re: [NMusers] Simulations with/without residual error

Andreas,

My suggestion:

If you want to compare your simulations with actual observations then

you should include residual error in the simulation. The observations

will include noise as well as the 'true' value so in order to compare

observations with simulated observations you need the residual error.

If you want to use the simulation to describe the 'true' value then dont

include the residual error. Residual error is assumed to have a mean of

zero around the 'true' value so there is no point in adding this kind of

noise if you are trying to predict the 'true' value.

Your examples suggest to me that you are trying to predict the 'true'

value -- not trying to match simulations directly with measured values.

If my guess is correct then you dont need to include residual error.

However, if you are using simulations for some kind of predictive check

(visual, numerical, statistical) that will be compared to distribution

statistics of the observations then you should include residual error.

Nick

andreas lindauer wrote:

--

Nick Holford, Professor Clinical Pharmacology

Dept Pharmacology & Clinical Pharmacology

University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New

Zealand

n.holford

mobile: +33 64 271-6369 (Apr 6-Jul 20 2009)

http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford

The information of this email and in any file transmitted with it is strict=

ly confidential and may be legally privileged.

It is intended solely for the addressee. If you are not the intended recipi=

ent, any copying, distribution or any other use of this email is prohibited=

and may be unlawful. In such case, you should please notify the sender imm=

ediately and destroy this email.

The content of this email is not legally binding unless confirmed by letter.

Any views expressed in this message are those of the individual sender, exc=

ept where the message states otherwise and the sender is authorised to stat=

e them to be the views of the sender's company. For further information abo=

ut Actelion please see our website at http://www.actelion.com

Received on Thu Jul 09 2009 - 04:05:37 EDT