From: Anne-Gaelle Dosne <*annegaelle.dosne*>

Date: Mon, 5 Aug 2013 09:37:34 +0200

Dear Dinko,

As Jakob already mentioned, the assumptions we are making when =

simulating

with parameter uncertainty using the variance-covariance matrix or the

bootstrap are the same as when we use these techniques to get confidence

intervals around model parameters. For the covariance matrix, we assume =

the

parameter vectors arise from a multivariate normal distribution given by =

the

asymptotic covariance matrix. For the bootstrap, parameter vectors arise

from each bootstrapped dataset, so there is no assumption about a global

parameter distribution.

Both of these methods can have drawbacks, in particular when one is far =

from

asymptotic conditions (problematic for covariance matrices) or when =

datasets

have few individuals, many stratas, or when the bootstrap is not =

problematic

(see Niebecker et al. PAGE 2013). Another alternative to simulate with

parameter uncertainty is to use Sampling Importance Resampling, which I

presented at PAGE this year. The principle is to simulate parameter =

vectors

from the covariance matrix, but add a step where they are evaluated on =

the

original data. Weights are then assigned to each parameter vector

representing how likely they are given the data at hand, and based on =

these

weights you can sample parameter vectors to use for simulation with

uncertainty.

Best regards,

Anne-Gaëlle

-------------------------------------------------------

Anne-Gaëlle Dosne, PharmD, PhD student

Pharmacometrics Research Group,

Department of Pharmaceutical Biosciences,

Uppsala University

PO Box 591 - 751 24 Uppsala - Sweden

Mobile: +46 725 859 870

Email: annegaelle.dosne

--------------------------------------------------------

From: owner-nmusers

On

Behalf Of Dinko Rekic

Sent: 01 August 2013 18:55

To: nmusers

Subject: [NMusers] Simulation with uncertainty

Dear NMusers,

I would like to get your thoughts on some common used techniques for

simulation with uncertainty. If one is interested in simulating the =

expected

mean response, there are two methods that are can be employed:

(1) Use the variance-covariance matrix

(2) Use of bootstrap results.

What assumptions are we making when using each of the methods? What are =

the

respective prose and cons? Do you have any preference in terms of when =

to

use method 1 over 2 or vice versa?

Thanks and kind regards

//Dinko

Received on Mon Aug 05 2013 - 03:37:34 EDT

Date: Mon, 5 Aug 2013 09:37:34 +0200

Dear Dinko,

As Jakob already mentioned, the assumptions we are making when =

simulating

with parameter uncertainty using the variance-covariance matrix or the

bootstrap are the same as when we use these techniques to get confidence

intervals around model parameters. For the covariance matrix, we assume =

the

parameter vectors arise from a multivariate normal distribution given by =

the

asymptotic covariance matrix. For the bootstrap, parameter vectors arise

from each bootstrapped dataset, so there is no assumption about a global

parameter distribution.

Both of these methods can have drawbacks, in particular when one is far =

from

asymptotic conditions (problematic for covariance matrices) or when =

datasets

have few individuals, many stratas, or when the bootstrap is not =

problematic

(see Niebecker et al. PAGE 2013). Another alternative to simulate with

parameter uncertainty is to use Sampling Importance Resampling, which I

presented at PAGE this year. The principle is to simulate parameter =

vectors

from the covariance matrix, but add a step where they are evaluated on =

the

original data. Weights are then assigned to each parameter vector

representing how likely they are given the data at hand, and based on =

these

weights you can sample parameter vectors to use for simulation with

uncertainty.

Best regards,

Anne-Gaëlle

-------------------------------------------------------

Anne-Gaëlle Dosne, PharmD, PhD student

Pharmacometrics Research Group,

Department of Pharmaceutical Biosciences,

Uppsala University

PO Box 591 - 751 24 Uppsala - Sweden

Mobile: +46 725 859 870

Email: annegaelle.dosne

--------------------------------------------------------

From: owner-nmusers

On

Behalf Of Dinko Rekic

Sent: 01 August 2013 18:55

To: nmusers

Subject: [NMusers] Simulation with uncertainty

Dear NMusers,

I would like to get your thoughts on some common used techniques for

simulation with uncertainty. If one is interested in simulating the =

expected

mean response, there are two methods that are can be employed:

(1) Use the variance-covariance matrix

(2) Use of bootstrap results.

What assumptions are we making when using each of the methods? What are =

the

respective prose and cons? Do you have any preference in terms of when =

to

use method 1 over 2 or vice versa?

Thanks and kind regards

//Dinko

Received on Mon Aug 05 2013 - 03:37:34 EDT