Speaker
Description
Meaningful prediction of when the target number of events will be reached is essential for both sample-size determination and operational planning in event-driven clinical trials. In oncology studies, progression-free survival (PFS) based on RECIST assessments is one of the most commonly used endpoints. Tumor evaluations for determining progression are typically performed at pre-scheduled visits, often every 6–12 weeks depending on the specific trial design, disease setting, and patient burden considerations. Because progression can generally only be confirmed at these discrete assessment times, the timing of when an event is observed is effectively interval-censored. As a consequence, critical design parameters such as treatment-effect estimates (e.g., hazard ratios), and the resulting statistical power may depend not only on the underlying disease dynamics but also on the particular assessment schedule chosen (Tanase et al., 2017).
In this work, we illustrate how not only point estimates and statistical inference but also the projected timing of reaching the required number of PFS events is influenced by different tumor-assessment schedules. Using realistic oncology trial settings, we show how varying assessment timings and frequencies can produce distinct patterns of event accrual.
Overall, our results suggest that the choice of tumor-assessment schedule may play a role beyond logistical considerations, with potential statistical and operational implications. Accounting for this dependency in sample-size planning and event-accrual forecasting may help provide more realistic timelines.
Tanase et al. (2017). A proposal for progression-free survival assessment in patients with early progressive cancer. Anticancer Research, 37(10), 5851-5855.
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