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用于外部验证风险预测模型的信息价值分析
Mohsen Sadatsafavi1, Tae Yoon Lee1, Laure Wynants2,3
1Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
概括
信息价值 (VoI) 方法量化临床预测模型验证中的不确定性. 完美的信息的预期值 (EVPI) 衡量了潜在的净收益损失,指导了进一步的验证需求.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
- 决策科学 决策科学 决策科学
背景情况:
- 在新人群中对风险预测模型的外部验证至关重要,但由于有限的样本大小而面临不确定性.
- 这种不确定性可能导致在采用模型方面做出低于最佳的决策,从而影响潜在的净收益 (NB).
- 信息价值 (VoI) 方法提供了一个框架来量化这种不确定性的后果.
研究的目的:
- 应用 VoI 方法来量化外部模型验证中的不确定性对净收益的影响.
- 定义和计算用于临床预测模型验证的完美信息的预期值 (EVPI).
- 评估基于EVPI的进一步验证研究的需要.
主要方法:
- 用于模型验证的EVPI定义为NB中因不知道最佳决策而预期的损失.
- 通过模拟研究开发和比较基于启动和非对称的EVPI计算方法.
- 将这些方法应用于一项案例研究,该研究验证了使用非美国数据对美国子样本的心肌梗塞死亡率预测模型.
主要成果:
- 模拟研究显示,在计算方法中,EVPI值一致,随着样本大小的增加,EVPI下降.
- 在案例研究中,该模型在当前信息下被认为是有益的 (增量NB = 0.0020).
- 验证EVPI为0.0005 (25%相对EVPI),每年缩放时转化为显著的潜在NB损失,突出进一步验证的价值.
结论:
- VoI方法,特别是验证 EVPI,可以有效地应用于量化外部临床预测模型验证中的不确定性.
- EVPI提供了一个客观的衡量标准,以指导关于进一步验证研究的必要性和范围的决定.
- 与NB一起报告验证EVPI可以增强对外部验证结果的解释.
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