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Abstract:
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We present a Bayesian framework for sequential monitoring that allows for use of external data, and that can be applied in a wide range of clinical trial applications. The basis for this framework is the idea that, in many cases, specification of priors used for sequential monitoring and the stopping criteria can be semi-algorithmic byproducts of the trial hypotheses and relevant external data, simplifying the process of prior elicitation. Monitoring priors are defined using the family of generalized normal distributions which comprise a flexible class of priors, naturally allowing one to construct a prior that is peaked or flat about the parameter values thought to be most likely. External data are incorporated into the monitoring process though mixing an a priori skeptical prior with an enthusiastic one using a weight that can be fixed or adaptively estimated based on the degree to which observed data are better supported by a skeptical perspective versus an enthusiastic one. Preposterior analysis of each trial design is performed to illustrate that the proposed Bayesian approaches provide reasonable frequentist operating characteristics without having that explicit focus.
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