Statistical methods in medical research
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All statistical analyses demand uncertain inputs or assumptions. This is especially true of Bayesian analyses. ⋯ This article discusses some robust techniques that have been suggested in the literature. One goal is to make apparent the relevance of some of these techniques to biostatistical work.
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Stat Methods Med Res · Dec 1994
ReviewReview of nonparametric methods for the analysis of crossover studies.
This paper reviews nonparametric methods for the analysis of crossover studies. Primary attention is given to crossover studies to compare two treatments for a response variable that has a metric measurement level. ⋯ Methods for several specific situations along these lines are discussed in terms of principles with potentially broader applicability. Several examples are provided to illustrate the performance of some of the methods.
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Stat Methods Med Res · Dec 1994
ReviewThe analysis of binary and categorical data from crossover trials.
A review is presented of methods for the analysis of discrete data from crossover trials. The definition and interpretation of the model for the data is used as a central theme. ⋯ It is seen how much recent methodology for analysing correlated categorical data can be applied successfully to the crossover setting. The current accessibility of each method is considered and the different approaches are illustrated and compared using two examples.
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This paper presents a review of crossover designs for use in medical applications which have three or more treatment periods. Only outcomes which can be analysed as continuous variables are considered. ⋯ In practice, it is often possible to eliminate carryover by interposing sufficiently long 'washout' periods between successive treatments, and suitable designs for this case are also mentioned. Much current practice revolves around a model which has been widely criticized: the shortcomings of this model and the implications of possible remedies, for design as well as analysis, are discussed.