权重预测回归神经网络用于正确的受审查数据
Feipeng Zhang1, Xi Chen1, Peng Liu2
1School of Economics and Finance, Xi'an Jiaotong University, Xi'an, China.
Statistics in medicine
|September 29, 2024
概括
一种新的加权预测回归神经网络 (WERNN) 方法通过灵活模拟复杂的共同变量效应来改进生存分析,优于对审查数据的现有方法.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 生存分析的分析.
背景情况:
- 审查预测回归在生存分析中很有价值,用于模拟共同变量效应.
- 由于独立性和线性假设,现有的加权预测回归 (WER) 有局限性.
研究的目的:
- 为了引入一种新的加权指向回归神经网络 (WERNN) 方法.
- 克服传统 WER 方法的限制性假设.
主要方法:
- 将深度神经网络纳入被审查的预测回归框架.
- 在预期损失函数中利用审查权重 (IPCW) 的反向概率来处理随机审查.
主要成果:
- 拟议的WERNN方法有效地捕捉了非线性共变量效应.
- 与现有的WER方法相比,WERNN显示了对右边审查数据的优异预测性能.
结论:
- 沃尔恩提供了一种灵活而准确的方法,用于用受审查的数据进行生存分析.
- 该方法的性能通过模拟和现实数据应用来验证.
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