在异质的考克斯模型中探索性子组的识别:一个相对简单的程序
Larry F León1, Thomas Jemielita1, Zifang Guo2
1Biostatistics and Research Decision Sciences, Merck & Co., Inc., New Jersey.
Statistics in medicine
|July 2, 2024
概括
这项研究引入了森林搜索,这是一种用于识别可能遭受伤害或受益于生存分析治疗的患者子组的新方法. 该方法有效控制错误,并提高检测治疗效果异质性的准确性.
科学领域:
- 生物统计学 生物统计学
- 生存分析的分析.
- 临床试验方法论 临床试验方法论
背景情况:
- 识别具有差异性治疗效果的患者子组对于个性化医学至关重要.
- 现有的方法可能难以检测治疗可能有害的子组.
- 在生存数据分析中需要灵活和强大的子组识别方法.
研究的目的:
- 提出一种新的程序,即"森林搜索",用于识别具有显著治疗效应的子组,包括遭受损害的子组.
- 开发一种简单,灵活,适用于生存分析的方法.
- 将拟议方法的性能与现有方法 (如虚拟双胞胎和通用随机森林) 进行比较.
主要方法:
- 在考克斯模型框架内选所有可能的子组,使用危险比率值表明危害.
- 应用分离一致性标准来识别"与损害最大一致"的子组.
- 使用数值集成来近似类型-1 错误和功率,并使用引导偏差纠正的Cox模型估计与Jackknife差异近似.
主要成果:
- 拟议的森林搜索方法与虚拟双胞胎和通用随机森林相比,表现良好.
- 该方法有效地控制了错误类型-1错误,用于错误识别异质性.
- 森林搜索对实质性的异质处理效应表现出更高的功率和分类准确性.
结论:
- 森林搜索是一种简单,灵活和有效的程序,用于识别具有显著治疗效应的子组,特别是那些有危害风险的子组.
- 该方法提供了对错误发现的改进控制,并增强了检测真实治疗效果异质性的能力.
- 该方法通过模拟和在瘤学和艾滋病毒临床试验数据中的现实应用得到了验证.
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