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Abstract:
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In clinical trials, continuous monitoring of safety or efficacy event incidence rate plays a critical role in making timely decisions. Because the endpoint of interest is often the event incidence associated with a given length of treatment duration (e.g., incidence proportion of an adverse event with 2 years of dosing), assessing the event proportion before reaching the intended treatment duration becomes challenging, especially when the event onset profile evolves over time with accumulated drug exposure. This problem is addressed using a predictive approach in the Bayesian framework, where experts' prior knowledge about both the frequency and timing of the event occurrence is combined with observed data. More specifically, during an interim look, each event-free subject will be counted with a probability derived using prior knowledge. The proposed approach is particularly useful in early stage studies for signal detection based on limited information. But it can also be used as a tool for safety monitoring during late stage trials. The approach is illustrated using a case study where the incidence of an adverse event is continuously monitored during an Alzheimer's disease trial.
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