In this paper, based on Simon's two-stage optimal design, we propose a new adaptive one which depends on the first stage results using the restrict conditions the conditional type I error and the conditional power.
Estimation of the conditional power (CP) is proposed as a decision tool to stop a study early or to continue it.
Many of these designs assign certain restrictive functional forms to the design elements of stage 2, such as sample size, critical value and conditional power functions.
A conditional power approach is used to prevent inflation of the overall trial type I error rate while maintaining adequate statistical power conditional on the statistically significant interaction.
Conditional power and predictive power methods are widely used to analyze interim data in determining whether a trial is likely to eventually demonstrate both statistically significant and clinically meaningful results.
However, the final trial results demonstrated that the conditional power for significant benefit from the 0.5% PRO 2000 gel was sufficient to warrant trial continuation [13].
The conditional power analysis showed that the trial was unlikely to provide convincing evidence of effectiveness even with the most optimistic projections.
Due to concerns about the feasibility of such expansion, we did a conditional power calculation and asked the DMC to review the data and make a recommendation on study continuation.
Then the minimum conditional power equals 43%, see next subsection.
Outcomes were conditional power (CP), and time and cost savings.
We generated a scatter plot of the empirical conditional power based on 5,000 replicates against the calculated conditional power.
