Interim monitoring of clinical trials: Decision theory, dynamic programming and optimal stopping


  • CHRISTOPHER JENNISON 1Department of Mathematical Sciences, University of Bath, Bath, U.K.
  • BRUCE W. TURNBULL 2Department of Operations Research and Information Engineering, Cornell University, Ithaca, U.S.A


Clinical trial, group sequential test, Bayes decision problem, dynamic programming, optimal stopping


It is standard practice to monitor clinical trials with a view to stopping early if results are sufficiently compelling. We explain how the properties of stopping boundaries can be calculated numerically and how to optimise boundaries to minimise expected sample size while controlling type I and II error probabilities. Our optimisation method involves the use of dynamic programming to solve Bayes decision problems with no constraint on error rates. This conversion to an unconstrained problem is equivalent to using Lagrange multipliers. Applications of these methods in clinical trial design include the derivation of optimal adaptive designs in which future group sizes are allowed to depend on previously observed responses; designs which test both for superiority and non-inferiority; and group sequential tests which allow for a delay between treatment and response.


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