对于复发性临床事件的预测模型的性能评估指南
Laura J Bonnett1, Thomas Spain2, Alexandra Hunt2
1Department of Health Data Science, University of Liverpool, Liverpool, L69 3GL, UK. l.j.bonnett@liverpool.ac.uk.
Diagnostic and prognostic research
|March 18, 2025
概括
对于和等慢性疾病中复发事件的统计模型应利用所有事件数据. 普伦蒂斯,威廉姆斯和彼得森模型在预测反复事件方面表现出卓越的表现.
科学领域:
- 生物统计学 生物统计学
- 临床流行病学临床流行病学
- 慢性疾病管理 慢性疾病管理
背景情况:
- 诸如和喘等慢性疾病的特点是出现反复的事件.
- 传统的统计模型往往只关注到第一个事件的时间,忽视后续事件.
- 对复发事件的有效统计模型对于准确的患者结果预测至关重要.
研究的目的:
- 为了比较分析和喘中复发事件的不同统计模型的性能.
- 评估各种模型在预测复发性恶化和发作风险方面的有效性.
- 确定最佳方法来评估反复事件预测模型的性能.
主要方法:
- 利用了两个临床数据集:喘恶化和发作.
- 应用了基于计数的模型 (负二项式,零膨胀负二项式) 和考克斯模型变体 (安德森-吉尔,普伦蒂斯,威廉姆斯和彼得森).
- 使用数值 (RMSE,MAE,偏差) 和图形 (校准图,布兰德-阿尔特曼图) 方法评估模型性能.
主要成果:
- 用数字和图形措施评估了复发性喘和事件的模型性能.
- 普伦蒂斯,威廉姆斯和彼得森模型显示出喘和的预测和观察结果之间的最高一致性.
- 这表明它在这些慢性疾病中模拟反复发生的事件的能力优越.
结论:
- 不适当的统计模型可能导致错误的结论,可能会伤害患者.
- 慢性疾病的预测模型必须包含所有反复发生的事件,以准确评估风险.
- 推的数值和图形方法,以及修改的校准措施,用于评估这些模型.
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