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The analysis of data from 2 × 2 cross‐over trials with baseline measurements
Author(s) -
Kenward Michael G.,
Jones Byron
Publication year - 1987
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/sim.4780060806
Subject(s) - covariance , estimator , statistics , mathematics , generalized least squares , analysis of covariance , simple (philosophy) , parametric statistics , least squares function approximation , ordinary least squares , covariance function , computer science , philosophy , epistemology
Abstract An account is given of the analysis of data from 2 × 2 cross‐over trials which include baseline measurements. We show that most of the previously proposed methods can be incorporated into a general framework of least‐squares estimation with a simple linear model. A simple analysis based on ordinary least‐squares estimators is described which can be used with either two‐sample t ‐tests and confidence intervals or with the corresponding non‐parametric procedures. It is shown how the use of generalized least‐squares estimators is equivalent to the use of covariance adjustment. These methods require no assumptions about the covariance structure of the measurements from each subject. The results of assessing the covariance structure present in examples of data from a number of trials are summarized. These results suggest that previously proposed simple covariance structures are unlikely to be appropriate in general.