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-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] 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.holf...@auckland.ac.nz tel:+64(9)923-6730 fax:+64(9)373-7090
mobile: +33 64 271-6369 (Apr 6-Jul 20 2009)
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
