alexa Statistical methods for two-sequence three-period cross-over designs with incomplete data.


Drug Designing: Open Access

Author(s): Chow SC, Shao J

Abstract Share this page

Abstract In clinical trials, and in bioavailability and bioequivalence studies, one often encounters replicate cross-over designs such as a two-sequence three-period cross-over design to assess treatment and carry-over effects of two formulations of a drug product. Because of the potential dropout (or for some administrative reason), however, the observed data set from a replicate cross-over design is incomplete or unbalanced so that standard statistical methods for a cross-over design may not apply directly. For inference on the treatment and carry-over effects, we propose a method based on differences of the observations that eliminates the random subject effects and thus does not require any distributional condition on the random subject effects. When no datum is missing, this method provides the same results as the ordinary least squares method. When there are missing data, the proposed method still provides exact confidence intervals for the treatment and carry-over effects, as long as the dropout is independent of the measurement errors. We provide an example for illustration.
This article was published in Stat Med and referenced in Drug Designing: Open Access

Relevant Expert PPTs

Relevant Speaker PPTs

Recommended Conferences

Relevant Topics

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version