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Abstract:
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Recent oncology trials are saturated with the development of targeted therapies. This has led to a growing interest in a class of designs called "basket trials", whereby treatment allocation is biomarker-driven rather than disease-driven. In these studies, investigators are essentially screening for specific populations that respond to a drug or combination of drugs. Previously, we developed a model-free approach for such a trial setting, whereby baskets are either treated as independent or aggregated after an interim assessment of heterogeneity of the response rates across all baskets (Statistics in Medicine, 2017). Based on these results, we investigated whether further efficiencies and gains are possible by implementing a more complex modeling approach, such as using Bayesian hierarchical modeling. This framework has the potential to provide investigators with the flexibility to work within more complicated biological settings, such as multiple targets and multiple drugs. We will present the results of these investigations and discuss the advantages and disadvantages of working with model-based designs as compared to model-free designs.
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