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Data-dependent stopping is a general term to describe any statistical or administrative reason for stopping a trial. Consideration of the reasons given earlier may lead you to stop the trial at an early stage, or at least change the protocol.

The review, interpretation, and decision-making aspects of clinical trials based on interim data are necessary but prone to error. If the investigators learn of interim results, then it could affect objectivity during the remainder of the trial, or if statistical tests are performed repeatedly on the accumulating data, then the Type I error rate is increased.

There is a natural conflict. On one hand, terminating the trial as early as possible will save costs and labor, expose as few patients as possible to inferior treatments, and allow disseminating information about the treatments quickly. On the other hand, there are pressures to continue the trial for as long as possible in order to increase precision, reduce errors of inference, obtain sufficient statistical power to account for prognostic factors and examine subgroups of interest, and gather information on secondary endpoints.

All of the available statistical methods for interim analyses have some similar characteristics. They

  1. require the investigators to state the objectives clearly and in advance of the study,
  2. assume that the basic study design is sound,
  3. require some structure to the problem beyond the data being observed
  4. impose some penalty for early termination.

No statistical method is a substitute for judgment. The statistical criteria provide guidelines for terminating the trial because the decision to stop a trial is not based just on statistical information collected on one endpoint.