Survival Models with Censoring Driven by Random Enrollment
In clinical studies with time-to-event end points we face uncertainties caused by the enrollment process that can often be viewed as a stochastic process. The observed endpoints are randomly censored and the amount of gained information is random and its actual value is not known at the design stage but becomes known only after the study completion. To take this fact into account we develop a method that maximizes the average information. We derive the average elemental Fisher information matrices for a few scenarios to illustrate the approach, assuming that enrollment can be modeled by a Poisson process.
We thank the referees for their many helpful comments and insightful suggestions leading to an improved paper.
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