Adaptive design can have an impact on the Type I error rate in a clinical trial. Type I error refers to the false positive rate, where the null hypothesis is incorrectly rejected when it is actually true.
In adaptive design, modifications can be made to the trial design, sample size, treatment allocation, or statistical analysis based on the accumulating data during the trial. These modifications are typically guided by predefined adaptation rules and interim analyses.
When adapting a trial, there is a risk of increasing the Type I error rate if the adaptations are not properly controlled. The more adaptations made during a trial, the higher the potential for Type I error inflation. However, adaptive designs often incorporate methods to control the Type I error rate.
One approach to addressing this concern is to use adaptive designs with group sequential or hierarchical testing, which allow for multiple interim analyses while controlling the overall Type I error rate. Such methods use statistical techniques like alpha-spending functions, which allocate the Type I error rate across different analysis points. By pre-specifying the allocation of error rates, these methods help maintain the overall Type I error rate at the desired level.
Overall, adaptive design, when properly planned and controlled, can allow for more efficient trials without compromising the Type I error rate. However, it is crucial to carefully design and monitor adaptive trials to ensure that modifications do not lead to inflated Type I error rates.
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