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使用机器学习和传统回归方法将EORTC QLQ-C30和QLQ-LC13映射到肺癌患者的SF-6D实用指数中

Longlin Jiang¹, Kexun Li^1,2, Simiao Lu^1,3

¹Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu. No. 55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China.

Health and quality of life outcomes

|July 2, 2025

概括

这项研究开发了一种Ridge回归模型,用于预测肺癌患者的EORTC QLQ-C30和QLQ-LC13问卷中的短形式六维 (SF-6D) 健康效用得分. 这种算法提供了一个有效的替代方案,用于估计SF-6D值,当直接测量是不可行的.

科学领域:

卫生经济学卫生经济学
在瘤学瘤学.
心理测量心理测量心理测量

背景情况:

像SF-6D这样的基于偏好的措施对于肺癌的健康经济评估至关重要.
临床试验中经常缺少这些措施,限制了经济评估.
这项研究通过开发预测算法来解决这个差距.

研究的目的:

开发和验证用于预测SF-6D健康实用分数的映射算法.
使用来自 EORTC QLQ-C30 和 QLQ-LC13 问卷的数据.
在没有直接测量的情况下,提供估计SF-6D值的方法.

主要方法:

用了一个中国肺癌患者队列 (n=625).
使用了各种回归和机器学习技术,包括Ridge回归.
五重交叉验证和性能指标 (R2,RMSE,MAE,MAPE) 用于模型评估.

主要成果:

里奇回归模型展示了最好的映射性能.
最佳模型的关键性能指标是R2=0.753,RMSE=0.074,MAE=0.057和MAPE=8.169%.这些指标均为最佳模型的关键性能指标.
平均和中位数的SF-6D实用性得分分别为0.774和7.795.

结论:

一个优化的映射算法已经成功开发出来,可以从EORTC QLQ-C30和QLQ-LC13中预测SF-6D实用分数.
该算法为肺癌研究中估计SF-6D值提供了一种可靠的方法.
当基于偏好的公用事业数据无法获得时,它是有效的替代方案.

关键词:

肺癌肺癌是一种肺癌.绘制地图是为了绘制地图.在 QLQ-C30 中.在 QLQ-LC13 中.生活质量生活的质量.在SF-6D中,它是SF-6D.公用事业公用事业公用事业

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