JSM 2005 - Toronto

Abstract #304688

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 483
Type: Contributed
Date/Time: Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #304688
Title: Optimal Design of Experiments Accounting for Potential Missing Trials
Author(s): InYoung Baek*+
Companies: SUNY, Stony Brook
Address: AMS DeptMath Tower, Stony brook, NY, 11794-3600, United States
Keywords: optimal design ; missing observation ; non-missing probability function
Abstract:

We propose a general method of designing an experiment when there are potentially failing trials. We use logit model as an example and construct different types of Bayesian c-optimal designs under nonmissing probability functions. We show that the usual optimal design without nonmissing information can be quite inefficient due to missing data patterns. We examine missing data patterns from previous dose response studies and subsequently design new multiple-objective dose response studies accounting for potential missing observations in a Bayesian approach. The performances of designs with and without accounting for potential missing trials are compared in terms of the precision of related parameter estimation.

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Revised March 2005