As a future trend of healthcare, personalized medicine tailors medical treatments to individual patients. It requires to identify a subset of patients with the best response to treatment. The subset can be defined by a biomarker (eg, expression of a gene) and its cutoff value. Topics on subset identification have received massive attention. There are over two million hits by keyword searches on Google Scholar. However, designing clinical trials that utilize the discovered uncertain subsets/biomarkers is not trivial and rarely discussed in the literature. This leads to a gap between research results and real‐world drug development. To fill in this gap, we formulate the problem of clinical trial design into an optimization problem involving high‐dimensional integration, and propose a novel computational solution based on Monte Carlo and smoothing methods. Our method utilizes the modern techniques of general purpose computing on graphics processing units for large‐scale parallel computing. Compared to a published method in three‐dimensional problems, our approach is more accurate and 133 times faster. This advantage increases when dimensionality increases. Our method is scalable to higher dimensional problems since the precision bound of our estimated study power is a finite number not affected by dimensionality. To design clinical trials incorporating the potential biomarkers, users can use our software "DesignCTPB". This software can be found on Github and will be available as an R package on CRAN. Although our research is motivated by the design of clinical trials, the method can be used widely to solve other optimization problems involving high‐dimensional integration.

中文翻译：

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具有潜在生物标志物作用的临床试验的最佳设计：一种新颖的计算方法

作为医疗保健的未来趋势，个性化医疗可为个别患者量身定制医疗服务。它需要确定对治疗反应最佳的患者子集。该子集可以由生物标记（例如，基因的表达）及其截止值定义。关于子集识别的主题受到了广泛关注。在Google学术搜索上，通过关键字搜索获得的点击量超过200万次。然而，利用发现的不确定子集/生物标记物设计临床试验并非微不足道，并且在文献中很少讨论。这导致研究结果与实际药物开发之间存在差距。为了填补这一空白，我们将临床试验设计问题公式化为涉及高维集成的优化问题，并提出了一种基于蒙特卡洛和平滑方法的新型计算解决方案。我们的方法利用图形处理单元上的通用计算的现代技术进行大规模并行计算。与已发布的解决三维问题的方法相比，我们的方法更加准确，并且速度提高了133倍。当尺寸增加时，该优点增加。我们的方法可扩展到更高维度的问题，因为我们估计的研究能力的精度范围是不受维度影响的有限数。要设计包含潜在生物标志物的临床试验，用户可以使用我们的软件“ DesignCTPB”。该软件可以在Github上找到，并将作为R软件包在CRAN上提供。尽管我们的研究是受临床试验设计的推动，