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Abstract:
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Development of combination therapy presents new opportunities for cancer treatment. In Phase I trials, the primary goal is to determine the maximum tolerated dose (MTD) and the right regimen of the combination therapy on the basis of severe toxicity data, known as dose-limiting toxicity (DLT). In some situations toxicities of single agent and combination are investigated in the same trial to expedite the development process and to use resources effectively. However, this can be difficult due to small trial size. In this talk, we show a real life example where the single agent and multiple combination regimens are explored in the same trial. Here, we will describe an approach to include information from different sources to improve decisions. This can be challenging due to heterogeneity among different data sources. We propose a robust meta-analytic combined approach (Neuenschwander 2016) to handle this situation and integrate DLT data generated within and outside the trial. Proposed methodology induces adaptive borrowing based on the similarity of the data, which allows robust decision-making. Performance of the proposed model will be explored by a real life example and simulation.
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