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Population Data Analysis using MCPEM in S-ADAPT: introductory workshop

From: stacey.tannenbaum
Date: Thu, 11 Sep 2008 17:11:32 -0400

Dear Modeling and Simulation colleagues,

MoSAiC is pleased to announce the sponsorship of a one-day hands-on
introductory workshop entitled: Population Data Analysis using Monte-Carlo
Parameteric Expectation Maximization Algorithm (MCPEM) in S-ADAPT on
Thursday, October 30, 2008 in Lambertville, NJ.

The course will be taught by Serge Guzy, Principal Scientist in Global
Modeling and Simulation at XOMA and President and CEO of POP-PHARM, Inc,
and Chee Ng, Principal Scientist in the Discovery Medicine and Clinical
Pharmacology Group at Bristol-Myers Squibb. It will focus on S-ADAPT and
how this tool can be used in the development of nonlinear-mixed effect
models for population data analysis.

The S-ADAPT program provides an environment for performing population data
analysis, with or without covariates, using PK/PD models with extensive
simulation tools and many current state-of-the art nonlinear mixed-effect
estimation methods such as MCPEM, SAEM and full bayesian MCMC algorithm.
Template model files for basic PK and PD models based on the various
Advan/Trans algorithms in NONMEM are available, to be used as is, or
modifiable by the user. Inter-occasion variability and population mixtures
may also be modeled in S-ADAPT. The population analysis module in S-ADAPT
provides standard error analyses, post-hoc analyses, and graphical viewing
of post-hoc results. S-ADAPT also has the capability to distribute the
computation effort of a single analysis or multiple analyses across
several computers or within a multi-core CPU in a single computer to
reduce analysis time.

To review the agenda, obtain course details, and to register for the
course, please visit the MoSAiC website at
Registration is now open and will close on October 15. We are limited to
80 participants so register soon!

We hope to see you there!

Best regards,
Stacey Tannenbaum and Marc Pfister
Co-Chairs, MoSAiC (Modeling and Simulation Applications in Clinical

Received on Thu Sep 11 2008 - 17:11:32 EDT

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