Crossover Studies with Continuous Variables: Power Analysis
Background: The crossover design is a sensitive means of determining the efficacy of new drugs because it eliminates between subject-variability. However, when the response in the first period carries on into the second (carryover effects) or when time factors can not be kept constant in a lengthy crossover (time effects), the statistical power of testing may be jeopardized. We recently demonstrated that the crossover design with binary variables is a powerful method in spite of such factors as carryover effects. Power analysis of crossover trials with continuous variables have not been explicitly studied.
Objective: Using the Grizzle 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 reponse.
Results: We demonstrate that the sensitivity of testing is largely dependent on the levels of correlation between drug response. Whenever the correlation coefficient is >0, we soon will have better sensitivity to test treatment effect than carryover effect or time effect of similar size. Whenever levels of correlation are not strong positive or negative the statistical power to demonstrate similarly-sized treatment and carryover effect, or treatment and time effect is approximately 80%, which is an acceptable level for reliable testing.
Conclusions: The crossover design is a powerful method for assessing positively correlated treatment comparisons, despite the risk of carryover and time effects.
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