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Abstract:
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In noninferiority clinical trials, researchers would often like to show that the new therapy would have been proven superior to placebo, had one been included in the trial, in addition to being noninferior to the active control. Such proof requires cross-trial inference, which previously required strong, often unrealistic assumptions. For example, most traditional methods require that the constancy assumption holds (i.e., that the effect of any one therapy remains constant from trial to trial). In therapeutic settings with changing patient populations, however, this assumption is often highly suspect. We present a Bayesian method for cross-trial inference when treatment effect changes as a function of the study population. The performance of this method is examined under a variety of circumstances.
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