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Abstract:
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A novel two-stage Bayesian adaptive trial design is developed for small population which has difficulty of recruitment, and could benefit by borrowing information from previously completed trials to support establishing substantial evidence of efficacy for the small population in situations where information extrapolation from external data resource is justifiable. At the time of the stage I analysis, the extent of information borrowing from external data is determined by assessing compatibility of the observed trial data with its prior predictive distribution, derived using the external data resource. At this time, the trial may be stopped for futility, enrollment may be stopped (with ongoing patients followed up for primary outcome ascertainment), or enrollment may proceed into stage II to reach a prespecified maximum sample size. We provide guidance on how practitioners can approach answering the question through balancing use of the external data (when compatible with the small population data) with the need to ensure the design leads to reasonable recommendations regarding key actions that might be taken regarding the trial.
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