RE: AW: Simulations with/without residual error

From: Smith, Mike K <mike.k.smith>
Date: Thu, 9 Jul 2009 20:56:34 +0100

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-nmusers
On Behalf Of Nick Holford
Sent: 09 July 2009 05:18
To: nmusers
Subject: Re: AW: [NMusers] Simulations with/without residual error

Andreas,

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,
> 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-nmusers
> [mailto:owner-nmusers
> 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.holford
fax:+64(9)373-7090
mobile: +33 64 271-6369 (Apr 6-Jul 20 2009) =
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford

Received on Thu Jul 09 2009 - 15:56:34 EDT

The NONMEM Users Network is maintained by ICON plc. Requests to subscribe to the network should be sent to: nmusers-request@iconplc.com.

Once subscribed, you may contribute to the discussion by emailing: nmusers@globomaxnm.com.