Signs of the Timings: Predicting Time of Completion in Multiphase Survival Trials
Presented to the Delaware ASA monthly meeting in September, 2006.
Studying maintenance of clinical effect typically requires clinical response for a minimum amount of time on treatment before randomization. If randomized, patients are then followed until treatment failure or withdrawal, and the trial halted after a pre-specified number of events. For ethical and cost reasons it is desirable to minimize the number of patients enrolled and randomized, and to predict the time of the last event under multiple scenarios.
We describe a data-driven stochastic simulation for two such trials in which: Each phase is modeled as a competing event process; Distributions of event times are derived from Kaplan-Meier survival curves from available data; Parameter uncertainty is modeled based on K-M survival estimates; Withdrawals and events occur at similar overall rates, though at different times; Predictions are updated as information is accrued.
Attached below is the presentation, and the paper published in the Proceedings of the Joint Statistical Meeting