From: Smith, Mike K <*mike.k.smith*>

Date: Thu, 9 Jul 2009 20:56:34 +0100

Andreas L,

It's perhaps easier to decide which levels of variability to use if you =

consider the problems you are trying to answer:

Q1: What is the "true" value (e.g. Tmax) for this model given the data?

- Simulate without residual error.

Q2: What is the distribution of values that are consistent with the =

current model for a given dataset? e.g. VPC

- Simulate with residual error but not parameter uncertainty

Q3: What is the distribution of possible future observations i.e. new =

subjects in a new trial? e.g. PPC

- Simulate with residual error *and* parameter uncertainty. Ideally =

including uncertainty on OMEGA.

Q1 aims to eliminate observation error and find out the "true" values =

for derived parameters such as Cmax, Tmax, AUC. This approach may also =

be useful to make deterministic calculations, say from single to =

multiple dose. Q2 talks about the current data where we know what THETA =

and OMEGA are (for a given model). Q3 talks about future, as yet =

unobserved, data where THETA and OMEGA may be different.

I hope this helps.

Mike

-----Original Message-----

From: owner-nmusers

On Behalf Of Nick Holford

Sent: 09 July 2009 05:18

To: nmusers

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

Andreas,

I think you know the answer to your own question!

As I indicated before the use of residual error depends on the purpose =

of the simulation. If you want to simulate future measurements then =

residual error should be included....

Nick

andreas lindauer wrote:

*> 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 *

*> [mailto:owner-nmusers *

*> 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

fax:+64(9)373-7090

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

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

Received on Thu Jul 09 2009 - 15:56:34 EDT

Date: Thu, 9 Jul 2009 20:56:34 +0100

Andreas L,

It's perhaps easier to decide which levels of variability to use if you =

consider the problems you are trying to answer:

Q1: What is the "true" value (e.g. Tmax) for this model given the data?

- Simulate without residual error.

Q2: What is the distribution of values that are consistent with the =

current model for a given dataset? e.g. VPC

- Simulate with residual error but not parameter uncertainty

Q3: What is the distribution of possible future observations i.e. new =

subjects in a new trial? e.g. PPC

- Simulate with residual error *and* parameter uncertainty. Ideally =

including uncertainty on OMEGA.

Q1 aims to eliminate observation error and find out the "true" values =

for derived parameters such as Cmax, Tmax, AUC. This approach may also =

be useful to make deterministic calculations, say from single to =

multiple dose. Q2 talks about the current data where we know what THETA =

and OMEGA are (for a given model). Q3 talks about future, as yet =

unobserved, data where THETA and OMEGA may be different.

I hope this helps.

Mike

-----Original Message-----

From: owner-nmusers

On Behalf Of Nick Holford

Sent: 09 July 2009 05:18

To: nmusers

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

Andreas,

I think you know the answer to your own question!

As I indicated before the use of residual error depends on the purpose =

of the simulation. If you want to simulate future measurements then =

residual error should be included....

Nick

andreas lindauer wrote:

measured with error?

method?

values.

residual error.

)

--

Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & =

Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag =

92019, Auckland, New Zealand n.holford

fax:+64(9)373-7090

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

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

Received on Thu Jul 09 2009 - 15:56:34 EDT