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DESIGN AND ANALYSIS OF INTRA‐SUBJECT VARIABILITY IN CROSS‐OVER EXPERIMENTS
Author(s) -
CHINCHILLI VER M.,
ESINHART JAMES D.
Publication year - 1996
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(19960815)15:15<1619::aid-sim326>3.0.co;2-n
Subject(s) - restricted maximum likelihood , estimator , statistical inference , inference , statistics , random effects model , computer science , maximum likelihood , variance (accounting) , random variate , quasi likelihood , econometrics , subject (documents) , statistical model , mixed model , mathematics , count data , random variable , artificial intelligence , poisson distribution , meta analysis , medicine , accounting , library science , business
Recently, interest has grown in the development of inferential techniques to compare treatment variabilities in the setting of a cross‐over experiment. In particular, comparison of treatments with respect to intra‐subject variability has greater interest than has inter‐subject variability. We begin with a presentation of a general approach for statistical inference within a cross‐over design. We discuss three different statistical models where model choice depends on the design and assumptions about carry‐over effects. Each model incorporates t ‐variate random subject effects, where t is the number of treatments. We develop maximum likelihood (ML) and restricted maximum likelihood (REML) approaches to derive parameter estimators and we consider a special case in which closed‐form expressions for the variance component estimators are available. Finally, we illustrate the methodologies with the analysis of data from three examples.