Andreas K,
It is not strictly true to say you cannot specify the parameter
uncertainties from a previous run to be included in a simulation.
If you take the variance-covariance matrix of the estimate from a
previous run ('the uncertainty matrix') you can add it as an additional
OMEGA matrix and use it to obtain parameter samples with uncertainty.
e.g. with a very simple example with just two parameters. This will
simulate 100 data sets and uncertainty to the THETA values for CL and V.
$SIM (20090709) ONLYSIM SUBPROBLEMS=100
; estimates of THETA and OMEGA from previous run
$THETA
1 ; POP_CL theta1
10 ; POP_V theta2
$OMEGA
0.5 ; PPV_CL eta1
0.5 ; PPV_V eta2
;variance-covariance matrix of the THETA estimates from previous run
$OMEGA BLOCK(2)
0.2 ; UNC_POP_CL eta3
0.1 3 ; UNC_POP_V eta 4
$PK
; get CL and V uncertainties
IF (NEWIND.EQ.0) THEN ; do this just once per subproblem
UNCCL=THETA(1)+ETA(3)
UNCV=THETA(2)+ETA(4)
ENDIF
CL=UNCCL*EXP(ETA(1)) ; with uncertainty for CL
V =UNCV*EXP(ETA(2)) ; with uncertainty for V
...
Nick
andreas.kra...@actelion.com wrote:
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.kra...@actelion.com / www.actelion.com
-----owner-nmus...@globomaxnm.com wrote: -----
To: <nmusers@globomaxnm.com>
From: "andreas lindauer" <linda...@uni-bonn.de>
Sent by: owner-nmus...@globomaxnm.com
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-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
The information of this email and in any file transmitted with it is strictly
confidential and may be legally privileged.
It is intended solely for the addressee. If you are not the intended recipient,
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 immediately 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, except where the message states otherwise and the sender is authorised to state them to be the views of the sender's company. For further information about Actelion please see our website at http://www.actelion.com
--
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
