Speaker
Description
Adaptive and, in particular, group-sequential designs are well-established in clinical trials. Time-to-event endpoints pose particular challenges because individual participants can contribute data to multiple stages of the trial. Nevertheless, the log-rank test - the standard analysis method for time-to-event data - can be embedded in flexible adaptive designs (e.g. with sample-size recalculation, SSR) as long as hypothesis test and SSR are based on a single time-to-event. This relies on the (asymptotic) property of independent increments of the score/log-rank process in calendar time under the null hypothesis and standard censoring assumptions.
Complexity increases substantially when multiple time-to-event endpoints are to be considered simultaneously. This involves multiplicity across endpoints as well as the use of surrogate endpoints different from the primary endpoint to guide adaptations. Such situations arise, for example, for the prominent endpoints progression-free survival (PFS) and overall survival (OS) in oncology. It has been shown that the family-wise error rate (FWER) can be inflated in this context.
We illustrate why pure group-sequential designs can control the FWER without assumptions about the joint distribution of the endpoints, whereas more flexible adaptive designs with SSR generally require additional assumptions. We support this with mathematical arguments and simulations for the PFS/OS setting. We further show that assuming a Markov multi-state model for PFS and OS is sufficient to permit the desired flexibility of adaptive designs while maintaining FWER control.
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