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STATISTICAL METHODS FOR TWO‐SEQUENCE THREE‐PERIOD CROSS‐OVER DESIGNS WITH INCOMPLETE DATA
Author(s) -
CHOW SHEINCHUNG,
SHAO JUN
Publication year - 1997
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19970515)16:9<1031::aid-sim519>3.0.co;2-6
Subject(s) - dropout (neural networks) , replicate , computer science , bioequivalence , carry (investment) , inference , missing data , confidence interval , statistics , sequence (biology) , data set , mathematics , machine learning , artificial intelligence , medicine , bioavailability , finance , biology , economics , pharmacology , genetics
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. © 1997 by John Wiley & Sons, Ltd. Stat. Med., Vol. 16, 1031–1039 (1997).