Department of Epidemiology and Biostatistics, Public Health School, Harbin Medical University, Harbin, Heilongjiang, China.
Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, Guangdong, China.
J Biopharm Stat. 2024 Jan 2;34(1):55-77. doi: 10.1080/10543406.2023.2170397. Epub 2023 Feb 1.
Modern precision medicine requires drug development to account for patients' heterogeneity, as only a subgroup of the patient population is likely to benefit from the targeted therapy. In this paper, we propose a novel method for subgroup identification based on a genetic algorithm. The proposed method can detect promising subgroups defined by predictive biomarkers in which the treatment effects are much higher than the population average. The main idea is to search for the subgroup with the greatest predictive ability in the entire subgroup space via a genetic algorithm. We design a real-valued representation of subgroups that evolves according to a genetic algorithm and derive an objective function that properly evaluates the predictive ability of the subgroups. Compared with model- or tree-based subgroup identification methods, the distinctive search strategy of this new approach offers an improved capability to explore subgroups defined by multiple predictive biomarkers. By embedding a resampling scheme, the multiplicity and complexity issues inherent in subgroup identification methods can be addressed flexibly. We evaluate the performance of the proposed method in comparison with two other methods using simulation studies and a real-world example. The results show that the proposed method exhibits good properties in terms of multiplicity and complexity control, and the subgroups identified are much more accurate. Although we focus on the implementation of censored survival data, this method could easily be extended for the realization of continuous and categorical endpoints.
现代精准医学要求药物开发考虑患者的异质性，因为只有一小部分患者群体可能受益于靶向治疗。在本文中，我们提出了一种基于遗传算法的新的亚组识别方法。该方法可以检测出具有预测生物标志物的有前途的亚组，其中治疗效果明显高于人群平均水平。主要思想是通过遗传算法在整个亚组空间中搜索预测能力最强的亚组。我们设计了一种基于遗传算法进化的亚组实值表示，并推导出一种适当评估亚组预测能力的目标函数。与基于模型或树的亚组识别方法相比，这种新方法的独特搜索策略提供了一种改进的能力，可以探索由多个预测生物标志物定义的亚组。通过嵌入重采样方案，可以灵活地解决亚组识别方法中固有的多重性和复杂性问题。我们使用模拟研究和一个真实示例比较了所提出的方法与两种其他方法的性能。结果表明，所提出的方法在多重性和复杂性控制方面表现出良好的性能，并且识别出的亚组更加准确。虽然我们专注于实现有 censored 的生存数据，但该方法可以很容易地扩展到实现连续和分类终点。
