Andreas L, It's perhaps easier to decide which levels of variability to use if you consider the problems you are trying to answer:
Q1: What is the "true" value (e.g. Tmax) for this model given the data? - Simulate without residual error. Q2: What is the distribution of values that are consistent with the current model for a given dataset? e.g. VPC - Simulate with residual error but not parameter uncertainty Q3: What is the distribution of possible future observations i.e. new subjects in a new trial? e.g. PPC - Simulate with residual error *and* parameter uncertainty. Ideally including uncertainty on OMEGA. Q1 aims to eliminate observation error and find out the "true" values for derived parameters such as Cmax, Tmax, AUC. This approach may also be useful to make deterministic calculations, say from single to multiple dose. Q2 talks about the current data where we know what THETA and OMEGA are (for a given model). Q3 talks about future, as yet unobserved, data where THETA and OMEGA may be different. I hope this helps. Mike -----Original Message----- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Nick Holford Sent: 09 July 2009 05:18 To: nmusers Subject: Re: AW: [NMusers] 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: 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
