title: Crossover studies with continuous variables: power analysis.
abstract: The crossover design is a sensitive means of determining the efficacy of new drugs because it eliminates between-subject variability. However, if the response in the first period carries on into the second (carryover effect) or if time factors cannot be kept constant in a lengthy crossover (time effects), its statistical power may be jeopardized. This may be equally true if a negative correlation exists between treatment responses. We recently demonstrated that the crossover design with binary variables is a powerful method even if correlation between treatment responses is negative. Power analysis of crossover trials with continuous variables has not been explicitly studied. Using the Scheffé model for the assessment of treatment effect, carryover effect, and time effect, we drew power curves of hypothesized crossover studies with different levels of correlation between drug response. We demonstrate that the sensitivity of testing is largely dependent on the levels of correlation between drug response. When positive, we have a great deal of sensitivity with which to test treatment effect and little sensitivity to test carryover or time effect. When negative, the opposite is observed. The correlation level in a crossover comparison is a major determinant of the sensitivity of testing. Treatments from one class with one mode of action frequently have a positive correlation and should be particularly considered for crossover comparisons. With treatment comparisons of totally different classes of drugs/modes of action, the opposite is true. It is hoped that this work affects the design of future crossover trials.
authors.affiliation: Department of Medicine, Merwede Hospital Dordrecht, University Hospital Leiden, The Netherlands.
authors.forename: T J M
authors.initials: TJ
authors.lastname: Cleophas
authors.forename: A H
authors.initials: AH
authors.lastname: Zwinderman
mesh_terms: D000465:Algorithms; D002986:Clinical Trials as Topic; D018592:Cross-Over Studies; D003627:Data Interpretation, Statistical; D006801:Humans; D008962:Models, Theoretical; D010601:Pharmacology, Clinical; D011336:Probability; D012107:Research Design; D013997:Time Factors