MDCcure:用于混合治愈模型中的马丁盖尔差异相关性和假设测试的R包
Blanca E Monroy-Castillo1, M Amalia Jácome2, Ricardo Cao1
1MODES Research group, CITIC (Center for Information and Communications Technology Research), Department of Mathematics, Faculty of Informatics, Universidade da Coruña, 15071 Campus de Elviña s/n, A Coruña, Spain.
Computer methods and programs in biomedicine
|October 28, 2025
概括
这项研究引入了高级疗法模型分析的R包,为共变效应和模型诊断提供了新的非参数方法. 该包为生物统计学家提供了有效的工具,以改善生存分析和治愈概率估计.
科学领域:
- 生物统计学 生物统计学
- 生存分析的分析.
- 计算统计学 计算统计学
背景情况:
- 评估共变量-临床结果关系在生物统计学中至关重要,特别是在生存和治愈模型中.
- 传统方法难以处理复杂的依赖关系和对共变量效应的非参数评估.
- 需要有效的计算工具来测试假设和诊断治愈模型.
研究的目的:
- 提出一个全面的R包,用于治疗模型的先进生物统计分析.
- 实施新的和现有的依赖性分析方法,非参数假设测试和合适性测试.
- 为了提高长期生存和治愈概率的估计.
主要方法:
- 开发了一个R包,其中包含了马丁盖尔差异相关性和分歧的函数,以分析对条件平均值的共变量效应.
- 提出了四种方法 (三种基于马丁加尔,一种基于L2距离) 的非参数框架,用于测试治疗概率上的共变量显著性.
- 包括适合性测试 (goft) 和用于治愈率模型的视觉比较工具 (plotCure).
主要成果:
- 在模拟和真实世界数据分析中,R套件表现出强的性能.
- 基于马丁盖尔的测量有效地确定了影响条件资产的共同变量.
- 非参数测试准确地检测到对治愈概率的共变效应,在多变量设置中可以更好地解释.
结论:
- R包为治疗模式分析提供了准确和高效的推断工具.
- 实施的方法改善了对共变量关系和模型合适性的评估.
- 这有助于更好地理解和估计长期生存和治愈概率.
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