NONMEM Users Network Archive

Hosted by Cognigen

RE: Simulation with uncertainty

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 =
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 =
parameter vectors arise from a multivariate normal distribution given by =
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 =
asymptotic conditions (problematic for covariance matrices) or when =
have few individuals, many stratas, or when the bootstrap is not =
(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 =
from the covariance matrix, but add a step where they are evaluated on =
original data. Weights are then assigned to each parameter vector
representing how likely they are given the data at hand, and based on =
weights you can sample parameter vectors to use for simulation with


Best regards,





Anne-GaŽlle 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
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 =
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 =
respective prose and cons? Do you have any preference in terms of when =
use method 1 over 2 or vice versa?

Thanks and kind regards

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

The NONMEM Users Network is maintained by ICON plc. Requests to subscribe to the network should be sent to:

Once subscribed, you may contribute to the discussion by emailing: