Title : Optimizing vaccine trial design: A novel promising zone approach with adaptive Interim analysis for managing delayed effects
Abstract:
Vaccines can exhibit varying delayed effects among individuals, posing challenges in trial design and analysis when using the log-rank test. This can lead to significant power loss and complexities in making interim decisions in adaptive designs. In this presentation, we introduce a novel promising zone design with an interim analysis approach. We utilize a piecewise proportional hazard model with random lag time to model individual survival and propose an adaptive design tailored for vaccines with diverse delayed effects. Our methodology includes solutions for calculating conditional power and adjusting the critical value for the log-rank test using interim data.
By categorizing the sample space into unfavorable, promising, and favorable zones based on survival parameter re-estimations and interim analysis results, we adjust sample sizes accordingly. Through simulations, we illustrate that our approach yields higher overall power compared to fixed sample designs and is comparable to matched group sequential trials. Furthermore, we validate that adjusting the critical value effectively manages the type I error rate. Lastly, we provide practical recommendations for implementing our method in cancer immunotherapy trials, emphasizing its potential to enhance trial efficiency and decision-making processes.
