Introduction
Tumors, glioblastoma
TUMORS, GLIOBLASTOMA, RECRUITMENT
In oncology, phase II clinical trials are often planned as single-arm two-stage designs with a binary endpoint, for example, progression-free survival after 12 months, and the option to stop for futility after the first stage. Simon’s two-stage design is a very popular approach but depending on the follow-up time required to measure the patients’ outcomes the trial may have to be paused undesirably long. To shorten this forced interruption, it was proposed to use a short-term endpoint for the interim decision, such as progression-free survival after 3 months. We show that if the assumptions for the short-term endpoint are misspecified, the decision-making in the interim can be misleading, resulting in a great loss of statistical power. For the setting of a binary endpoint with nested measurements, such as progression-free survival, we propose two approaches that utilize all available short-term and long-term assessments of the endpoint to guide the interim decision. One approach is based on conditional power and the other is based on Bayesian posterior predictive probability of success. In extensive simulations, we show that both methods perform similarly, when appropriately calibrated, and can greatly improve power compared to the existing approach in settings with slow patient recruitment. Software code to implement the methods is made publicly available.In oncology, phase II clinical trials are often carried out to test in a specific population of patients whether a therapy is sufficiently effective to warrant further investigation. Due to both ethical and practical constraints, sample sizes of such trials are typically strictly limited. Therefore, many trials apply a single-arm design to explore the treatment’s efficacy before initiating a larger randomized phase II or III clinical trial. Endpoints in such single-arm trials are usually binary, such as response according to Response Evaluation Criteria In Solid Tumors criteria or rate of progression-free survival (PFS) at a specific time point. PFS itself is naturally a time-to-event endpoint, but the measurements of progression are often not continuous but occur in a few discrete time steps, for example, every 3 months. In many trials an option to stop for futility after an interim analysis is implemented. A popular design that allows stopping for futility for a single-arm trial with a binary endpoint is Simon’s two-stage design.If the primary endpoint is assessed sometime after recruitment or treatment initiation, for example, if the endpoint is PFS after 12 months confirmed via magnetic resonance imaging, it may happen that there is a severe delay due to the interim analysis, since the second stage usually cannot be initiated before a sufficient number of survivors was observed in the first stage. This enforced trial interruption not only prolongs drug development, but might also negatively affect the momentum and discourage involved partners. In such situations, it would be attractive to use information on short-term endpoints to guide the decision making whether to stop for futility or to continue, so that there is little or no delay during the trial conduct. As a solution to this problem, Kunz et al.A similar approach to Kunz et al.When we planned an actual phase II clinical trial in the field of glioblastoma research and considered implementing the design by Kunz et al., the clinicians were very hesitant to provide any assumptions about the short-term endpoint, which would determine the threshold for stopping for futility and hence the fundamental design of the clinical trial. Even the assumption about the long-term endpoint 12-months PFS was a rather rough estimate based on a single study in similar patients, but for any earlier assessments of PFS, not even historical data was available because published studies only reported data for 12-months PFS. This was our motivation to seek a design that allows incorporation of short-term endpoints into the decision without having to make additional assumptions.A concept that is naturally suited to guide interim decisions in clinical trials such as stopping for futility, is conditional power (CP), which is the statistical power at a time of some interim analysis given the already observed data.Using both the CP and the PoS framework, we propose two new methods for futility stopping in single-arm phase II oncology settings: STE–CP and STE–PoS (combining In Section 2, we present an overview of the existing approaches on which the new methods are built upon. In Section 3, we introduce the new methods STE–CP and STE–PoS. In Section 4, we present a simulation study to assess the operating characteristics of the new methods and compare them to the existing designs, considering various scenarios for the true underlying survival functions as well as different patient accrual rates. We conclude with a discussion in Section 5.