NONMEM Users Network Archive

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AAPS free webinar on covariate selection: presented by M. Gastonguay and K Kowalski

From: Zhang, Liping <liping.zhang3>
Date: Fri, 22 Jan 2010 14:18:04 -0500

Sponsored by the
Population Pharmacokinetics and Pharmacodynamics Focus Group


***************************************************************************=
************
* Wednesday, February 10, 2009 from 12:30 - 2:00 pm EST
*
*
* The Full Covariate Models and WAM Algorithm=
:
* Efficient Building of Covariate Models and Appropriate Inferences a=
bout Covariate Effects
*
*
* Conducted by
* Marc R. Gastonguay, Ph.D., Metrum Research Group & Metrum Insti=
tute
* Kenneth G. Kowalski, M.S., Ann Arbor Pharmacometrics Group

* Moderated by
* Liping Zhang, Ph.D., Bristol-Myers Squibb
*
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************

log in 15 min before it starts, at www.gotomeeting.com/wizard

The Q&A Session will follow the formal lecture at approximately 1:15 pm EST=
. You may ask questions at any time during the webinar by typing them in to=
 the question box on your screen. You may also ask questions in advance dur=
ing the registration process.

PDF Handouts: Beginning approximately five days prior to the live event, yo=
u may download the presenter's handouts at:
http://mediaserver.aapspharmaceutica.com/meetings/webinars/ppdm-4/ppdm-4.pd=
f

Firewall or Other Connection Issues? Please visit the following site for tr=
oubleshooting information: https://www1.gotomeeting.com/default/help/g2m/tr=
oubleshooting/connection_test_help.htm


Description:

This presentation will provide an overview of Full Covariate Modeling metho=
d (Gastonguay, 2004) and Wald's Approximation Method (WAM) for covariate mo=
del building based on the methodology of Kowalski and Hutmacher (2001). An =
example will be used to illustrate application of the WAM algorithm and the=
 value obtained from evaluating all reduced models among the complete set o=
f possible hierarchical covariate models leading to a parsimonious final mo=
del. The presentation will also illustrate the utility of the Full Covariat=
e Model approach for inference about covariate effects and some possible st=
rategies for the successful development of a full model necessary to employ=
 the WAM approach and subsequent model-based simulation goals. The presenta=
tion will highlight the advantages and disadvantages of the Full Covariate =
Model approach and WAM approach and in comparison to standard stepwise proc=
edures. The presentation will conclude with a discussion of the available s=
oftware to implement the WAM algorithm and Full Covariate Model.

The WAM algorithm was first published in 2001; however, it has not been wid=
ely used in the modeling community in part because it requires the developm=
ent of a full model. At the time of its introduction, development of a full=
 model in which all covariate effects are estimated simultaneously was not =
routinely performed. Today, there is a greater appreciation of the value in=
 developing full models, particularly for inferential purposes regarding co=
variate effects. This webinar will present two promising approaches for cov=
ariate model building that leverage information from a full model as altern=
atives to standard stepwise procedures.

Goals and Objectives:
* To provide an overview of the goals of covariate modeling in the dr=
ug development process;
* To provide an overview of Full Covariate Model and WAM algorithm ap=
proaches relative to traditional Stepwise Regression, define advantages and=
 disadvantages of each method as a function of covariate modeling objective=
s in drug development;
* To promote an awareness of the methodologies and the available soft=
ware.



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Received on Fri Jan 22 2010 - 14:18:04 EST

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