JSM 2005 Online Program

Abstract #304135

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter

Activity Number:	91
Type:	Topic Contributed
Date/Time:	Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor:	General Methodology
Abstract - #304135
Title:	Optimal Design of Pharmacokinetic Studies Described by Stochastic Differential Equations
Author(s):	Sergei Leonov*+ and Valerii Fedorov and Vladimir Anisimov
Companies:	GlaxoSmithKline and GlaxoSmithKline and GlaxoSmithKline
Address:	1250 So Collegeville Rd, Collegeville, PA, 19426, United States
Keywords:	optimal design ; nonlinear model ; intrinsic randomness ; pharmacokinetic model ; optimal sampling times
Abstract:	We discuss pharmacokinetic (PK) studies with serial sampling described by compartmental models and which lead to nonlinear mixed effects models with multiple responses. We consider three sources of randomness: intrinsic randomness, that is stochastic differential equations are used to build compartmental models; observational or measurement errors; and population variability (i.e., individual PK parameters of each patient are sampled from the population distribution). The quality of the information in an experiment is measured by the variance-covariance matrix of parameter estimates. We demonstrate how to optimize the precision of parameter estimates by finding the best number and allocation of sampling times.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program