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The talk briefly covers standard power sample size for time to event endpoints. It discusses assumptions driving patient numbers needed for events in a certain time frame. A strategy for estimating analysis timing with interim data is outlined. Results from a simulation study compare observed data distribution to exponential distributions used in sample size determination, highlighting significant differences. Concluding, distributional form of event and dropout data play a crucial role in time to event endpoints, commonly used in oncology trials.
