Simulations with/without residual error

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

Dear NMUsers, I cannot see a reason to leave out the residual error. We all know how error shifts around between IIV, IOV, and residual error. A large residual error is indeed troublesome even when all other model parameters would indicate a good fit with small shrinkage. You only recognize the trouble when you start simulating the kind of responses listed by Andreas. You may have to conclude in the end that your model is not very predictive because of large residual error (=large, still unexplained variability). Joachim Grevel ____________________________________________ AstraZeneca R&D Charnwood Clin. Pharmacology and DMPK Bakewell Road Loughborough, LE11 5RH Tel: +44 1509 64 5177 [email protected] -------------------------------------------------------------------------- AstraZeneca UK Limited is a company incorporated in England and Wales with registered number: 03674842 and a registered office at 15 Stanhope Gate, London W1K 1LN. Confidentiality Notice: This message is private and may contain confidential, proprietary and legally privileged information. If you have received this message in error, please notify us and remove it from your system and note that you must not copy, distribute or take any action in reliance on it. Any unauthorised use or disclosure of the contents of this message is not permitted and may be unlawful. Disclaimer: Email messages may be subject to delays, interception, non-delivery and unauthorised alterations. Therefore, information expressed in this message is not given or endorsed by AstraZeneca UK Limited unless otherwise notified by an authorised representative independent of this message. No contractual relationship is created by this message by any person unless specifically indicated by agreement in writing other than email. Monitoring: AstraZeneca UK Limited may monitor email traffic data and content for the purposes of the prevention and detection of crime, ensuring the security of our computer systems and checking Compliance with our Code of Conduct and Policies.

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 [email protected] 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