powerROC:一种交互式网络工具,用于计算样本大小,以评估模型的区分能力.
François Grolleau1, Robert Tibshirani2,2, Jonathan H Chen1,3,4
1Stanford Center for Biomedical Informatics Research, Stanford University.
概括
准确的样本大小对于验证使用接收器操作特征曲线 (AUROC) 下面面积的预测模型至关重要. 本研究介绍了powerROC,这是一个简化AUROC验证样本大小计算的工具,确保可靠的模型通用性.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
- 临床流行病学临床流行病学
背景情况:
- 外部验证对于评估预测模型通用性至关重要,通常使用AUROC等歧视指标.
- 目前基于AUROC的验证研究的样本大小计算经常缺乏严格性,导致分析不足.
- 可靠的样本大小确定对于准确的外部验证和可靠的临床预测至关重要.
研究的目的:
- 在基于AUROC的外部验证中审查基本概念,以精确确定样本大小.
- 为研究人员和临床医生提高样本大小计算理论和实践的可访问性.
- 引入powerROC,这是一个开源工具,用于简化模型验证中的样本大小计算.
主要方法:
- 在AUROC验证中对样本大小的确定进行统计概念的审查.
- 介绍powerROC,这是一个用于计算单个和双重模型比较样本大小的网络工具.
- 使用MIMIC数据库的医院死亡率预测案例研究来证明ROC的力量.
主要成果:
- powerROC 方便用于AUROC验证的样本大小计算,容纳单个模型评估和两种模型比较.
- 该工具支持灵活的方法,利用试点数据或用户定义的概率分布.
- 案例研究说明了powerROC用于评估预测模型性能的实际应用.
结论:
- 准确的样本大小确定对于使用AUROC的预测模型的强有力的外部验证至关重要.
- 功率ROC工具简化了复杂的计算,促进了更可靠和更容易访问的样本大小规划.
- 改进的样本大小方法提高了临床预测模型的概括性和可靠性.
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