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Updated: May 11, 2025

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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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为变量选择和风险预测进行规范化的胜率回归,并应用于心血管试验
1Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut St, Room 207 A, Madison, 53726, WI, USA. lmao@biostat.wisc.edu.
BMC medical research methodology
|April 17, 2025
概括
我们开发了wrnet,这是一种使用win比率框架分析层次复合端点的新方法. 它有效地处理了许多预测因素,超过了传统的考克斯回归,并提高了变量选择和预测准确度.
科学领域:
- 生物统计学 生物统计学
- 临床试验 临床试验
- 统计建模 统计建模
背景情况:
- 在临床试验中,分层复合终点至关重要,优先考虑死亡率等关键结果.
- 对于赢比率分析的现有回归框架与高维数据集和众多预测器作斗争.
- 对于赢比率回归,需要一个强大的变量选择方法.
研究的目的:
- 为赢比率回归提出弹性网型规范化方法.
- 扩展比例胜分数 (PW) 模型以处理高维数据.
- 为赢得比率分析提供可扩展和强大的变量选择方法.
主要方法:
- 开发了一种弹性网型规范化,用于赢比率回归,扩展PW模型.
- 针对处罚回归进行了适应的对对比较,并在模型选择中使用了主体级交叉验证.
- 使用通用一致性索引定义性能指标,并在wrnet R-package中实现该方法.
主要成果:
- 模拟研究表明,wrnet在时间到第一个事件分析中表现优于传统的考克斯回归.
- 在不同对死亡率和非致命事件的共同变量影响的场景中,wrnet表现优异.
- 对HF-ACTION试验数据的应用确定了预后变量,并提高了比规范化的Cox模型更准确的预测准确性.
结论:
- wrnet方法将胜利比率的可解释性与弹性网规范化的可扩展性相结合.
- wrnet R-package提供了一个用户友好的工具,用于应用这些先进的统计程序.
- 未来的工作可能会在赢得比率框架内解决共同变量效应中的非比例性和非线性.
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