Weighted Upstrap Futility Monitoring: Algorithmically Accounting for Data Driven Time Trends

Music City Center 

Futility monitoring is essential in clinical trial design to allow early termination for treatment inefficacy. Due to time varying patterns in relative risk it is relevant to consider time trends. Current futility analysis methods are not designed to identify time trends. We propose weighted upstrapping as a solution. Weighted upstraping involves assigning a time dependent weight to all observations and repeatedly sampling from the interim data to simulate thousands of fully enrolled trials. A p-value is calculated for each upstrapped dataset and the proportion of upstrapped trials meeting a significance criterion is compared to a decision threshold to determine futility. We implemented a simulation study with varying sample sizes and relative risk trends, for both null and alternative cases. We applied upstrapped futility designs as well as traditional group sequential designs for comparison. Weighted upstrapping more accurately identified futility for non-constant relative risk trends. Weighted upstrap designs were 7.1% more likely than group sequential designs to stop in the non-constant relative risk null setting and 2.6% less likely to stop in the equivalent alternative case.

Clinical trials

Interim futility monitoring

Weighted upstrap

Time trends

Nonparametric

Alpha-spending 

Biopharmaceutical Section