Optimal two-stage design of single arm Phase II clinical trials based on median event time test

Data-driven monitoring for phase II clinical trial designs based on percentile event time test

The goal of phase II clinical trials is to evaluate the therapeutic efficacy of a new drug. Some investigators want to use the time-to-event endpoint as the primary endpoint of the phase II study to see the improvement of the therapeutic efficacy of a new drug in median survival time. Recently, median event time test (METT) has been proposed to provide a simple and straightforward rule which compares the observed median survival time with the prespecified threshold. However, median survival time would not be observed during the trial if the drug performs well and indeed cures most patients or if the accrual rate is so fast. To address the issues in clinical practice, we first propose a percentile event time test (PETT), which generalizes METT to any percentile of the survival time, and develop data-driven monitoring for phase II clinical trial designs based on PETT. We evaluate the performance of the method through simulations and illustrate the proposed method with a trial example.

Sample size determination and evaluation for two-stage adaptive designs of single arm clinical trials based on median event time test

Clinical trials play a critical role in drug development which involves a series of phases and requires a significant amount of time and effort. Efficient clinical trial designs are necessary to investigate a new drug. Investigators strongly desire to use the time-to-event endpoint as the primary endpoint for Phase II studies, which evaluates the therapeutic efficacy of the new drug, with the hypothesis that the new drug improves the median survival time. The one-sample log-rank test has been used for single-arm Phase II trials, but it generally requires more samples. Recently, the median event time test was proposed to provide a simple, straightforward decision rule, which compares the observed median survival time for the new drug with the threshold, which is determined through the numerical search. We improve the computation of the method for the two-stage design of single-arm clinical trials based on the median event time test. By utilizing the large sample theory of order statistics, we provide the explicit formulas to calculate the sample size for the first and second stages and propose the testing procedure. The performance of the proposed method is evaluated through simulations and a trial example.

Adapting Simon's Two-Stage Design for Efficient Screening of Filovirus Vaccines in Non-Human Primates

The cynomolgus monkey (Macaca fascicularis) non-human primate (NHP) is widely used for filovirus vaccine testing. To use limited BSL-4 resources efficiently and minimize NHP usage, Simon's two-stage design was adapted to screen candidate Ebola virus (EBOV) vaccines in up to six NHPs with two (optimal), three, or four NHPs in Stage 1. Using the optimal design, two NHPs were tested in Stage 1. If neither survived, the candidate was rejected. Otherwise, it was eligible for Stage 2 testing in four NHPs. Candidates advanced if four or more NHPs were protected over both stages. An 80% efficacious candidate vaccine had 88.5% probability of advancing, and a 40% efficacious candidate vaccine had 83% probability of rejection. Simon's two-stage design was used to screen 27 EBOV vaccine candidates in 43 candidate regimens that varied in dose, adjuvant, formulation, or schedule. Of the 30 candidate regimens tested using two NHPs in Stage 1, 15 were rejected, nine were withdrawn, and six were tested in Stage 2. All six tested in Stage 2 qualified to advance in the product development pipeline. Multiple regimens for the EBOV vaccines approved by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) in 2019 were tested in this program. This approach may also prove useful for screening Sudan virus (SUDV) and Marburg virus (MARV) vaccine candidates.