NONMEM Users Network Archive

Hosted by Cognigen

Parameter Uncertainty and Covariate effects

From: Stodtmann, Sven <sven.stodtmann>
Date: Mon, 11 Jan 2016 12:52:40 +0000

Dear All,

In order to account for uncertainty in estimated parameters when running a simulation, a natural approach would be running multiple simulations for different parameter vectors which are drawn from the (theoretical, asymptotic) distribution of the estimator (i.e. normal with mean THETA and covariance according to the NONMEMs $COR output for the THETAs).
This approach may in some cases (particularly, when there are a lot of covariate effects estimated) lead to very broad parameter distributions, even assigning some quite high probability of unphysiological values if one didn’t have good quality data, strong priors or a very careful parametrization of the model (e.g. transforming/bounding parameters, which requires/introduces prior knowledge as well).

Another problem connected with parameter uncertainty on covariate effects is the following. Say we model
CL = TVCL * SEX_EFF**SEX, (Eq. 1)
where male is coded as SEX=0, female as SEX=1.
In this case, when using the above mentioned technique to account for parameter uncertainty, the female population will have a more variable (uncertain) PK, not just different one. If we phrase the problem differently, using
CL = TVCL * SEX_EFF**(1-SEX) , (Eq. 2)
The conclusion would be the other way around (i.e. male PK is more uncertain).

One approach to deal with the second problem could be this:
In order to remove this (usually unjustified) assumption (the female population having a less certain PK compared to the male), one could try to model the same covariate effect as follows:
In this case TVCL would already include “half” of the effect (on the log scale; the “new” TVCL would be TVCL*SQRT(SEX_EFF) in terms of the parameters used in Eq.1).
With this approach, both sub-populations, male and female get “some part” of the uncertainty effect.
Of course it would be even nicer to let the data decide which sub-population gets how much uncertainty exactly instead of evenly splitting it.

How do you deal with uncertainty in the estimates of covariate effects when it comes to simulations/predictions?

Kind Regards,

AbbVie Deutschland GmbH & Co KG
Clinical Pharmacology and Pharmacometrics
Knollstrasse 50
67065 Ludwigshafen am Rhein, Germany
OFFICE +49 621-589-1940


Sitz der Gesellschaft: Wiesbaden - Registergericht: AG Wiesbaden HRA 9790
Persönlich haftende Gesellschafterin: AbbVie Komplementär GmbH
Sitz der persönlich haftenden Gesellschafterin: Wiesbaden - Registergericht: AG Wiesbaden HRB 26371
Geschäftsführer: Dr. Patrick Horber, Thomas Scheidmeir, Dr. Stefan Simianer, William J. Chase
Vorsitzende des Aufsichtsrats: Dr. Azita Saleki-Gerhardt

This communication may contain information that is proprietary, confidential, or exempt from disclosure. If you are not the intended recipient, please note that any other dissemination, distribution, use or copying of this communication is strictly prohibited. Anyone who receives this message in error should notify the sender immediately by telephone or by return e-mail and delete it from his or her computer.

Diese Kommunikation kann Informationen enthalten, die geheim, vertraulich oder hinsichtlich der Offenlegung beschränkt sind. Wenn Sie nicht der beabsichtigte Empfänger sind, nehmen Sie bitte zur Kenntnis, dass jede Weitergabe, Verteilung, Verwendung oder Vervielfältigung dieser. Kommunikation strikt untersagt ist. Jeder, der diese Nachricht fehlerhaft erhält, sollte den Sender unverzüglich telefonisch oder durch Rücksendung der E-Mail benachrichtigen und diese von seinem oder ihrem Computer löschen.
Received on Mon Jan 11 2016 - 07:52:40 EST

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: