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Innovative statistical approaches for studies in anti-infective drug combination development

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Combination trials tend to be seen as the pre-dominance of oncology; however, in other therapeutic areas they have many benefits. The treatments for many viral diseases are effective and safe, however, in some there is still a clinical unmet need. Levels of successful vaccination have reduced the need for treatments in some viral diseases, however, where there is a need, current therapies show low efficacy and poor tolerability. Monotherapies are seen to be partially effective, however, it is thought that combinations would provide better efficacy, whilst still maintaining a good safety profile.

The development of combinations in anti-infective drugs rely more on combining two new molecular entities rather than adding onto an existing standard of care. This presents many problems; however, some learning from oncology can help with the development of these combinations.

In this presentation I will show how using an adaptive platform study, combined with Bayesian methods will increase the chances of effective combinations coming to market. Platform trials, where many treatment arms are compared to a common control are seen as an efficient way of testing new therapies. The use of Bayesian stopping rules for futility allow for the stopping of ineffective arms. The use of borrowing also adds to the efficiency. These ideas may be coupled with the use of seamless Phase II/III designs to further increase efficiency in moving towards the registration of new combination therapies.

This talk is part of the Cambridge Statistics Discussion Group (CSDG) series.

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