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用于外部验证的贝叶斯样本大小计算 风险预测模型的外部验证研究
Mohsen Sadatsafavi1, Paul Gustafson2, Solmaz Setayeshgar3
1Faculty of Pharmaceutical Sciences and Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada.
Statistics in medicine
|February 12, 2026
概括
本研究引入了贝叶斯框架,用于风险预测模型验证的样本大小计算. 它提供了灵活的规则,考虑不确定性,提高准确性和临床实用性评估,以更好地评估模型.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 目前用于外部验证风险预测模型的样本大小计算依赖于固定的绩效指标和精度目标.
- 这种方法受到由于先前研究样本有限而导致真正模型性能不确定性的限制.
- 传统方法不太适合使用净益 (NB) 等措施来评估临床效用.
研究的目的:
- 为具有二进制结果的预测模型提出一个一般的贝叶斯框架,以考虑多个标准的样本大小.
- 制定统计绩效指标 (歧视,校准) 和临床实用性 (净效益) 的样本大小规则.
- 通过结合不确定性和提供灵活的样本大小确定来解决传统方法的局限性.
主要方法:
- 开发了一个贝叶斯框架来计算样本大小.
- 拟议的样本大小规则针对性能指标的预期精度或保证概率.
- 引入了基于最佳性保证和信息价值 (VoI) 分析的净收益规则.
- 应用了框架来验证COVID-19患者恶化风险模型.
主要成果:
- 贝叶斯式方法量化了模型性能中的不确定性,使得样本大小规则灵活.
- 对净收益的信息价值 (VoI) 分析表明,与基于精度的校准指标相比,样本大小较小.
- 在COVID-19风险预测模型的案例研究中证明了应用.
结论:
- 贝叶斯框架为外部模型验证的样本大小计算提供了更全面的方法.
- 这种方法允许基于预期精度,保证概率和VoI的灵活样本大小的确定.
- 拟议的方法,特别是净收益的VoI,可以导致更高效的样本大小要求.
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