Data-driven analysis strategies for proportion studies in adaptive group sequential test designs

Using multistage adaptive group sequential test designs, the investigator may perform data-driven changes in the design during the course of the trial without inflation of the Type I error rate. This is possible, for example, through the use of the inverse normal method of combining the p-values from the separate stages of the trial. Generally, conditional error functions are useful instruments for midtrial design modifications of clinical trials. Particularly, it is worthwhile to consider sample size reassessment strategies based on conditional power arguments. In this paper, approximate techniques will be proposed for the application of the inverse normal combination testing principle in superiority and noninferiority proportion studies. Planning facilities and the adaptive analysis strategies will be discussed in terms of the Type I error rate, the necessary sample size, and the power within the adaptive design. Furthermore, how to calculate confidence intervals and overall p-values will be shown.