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AW: Simulations with/without residual error

From: andreas lindauer <lindauer>
Date: Thu, 9 Jul 2009 09:42:56 +0200

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
Im
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


Received on Thu Jul 09 2009 - 03:42:56 EDT

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