Re: AW: 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: [email protected] [mailto:[email protected]] 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 [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