powerROC:一个用于评估模型判别能力的样本量计算的交互式网络工具。

François Grolleau, Robert Tibshirani, Jonathan H Chen
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引用次数: 0

摘要

严格的外部验证对于评估预测模型的泛化性至关重要,特别是通过评估它们对新数据的辨别能力(AUROC)。这通常涉及将新模型的AUROC与已建立的参考模型的AUROC进行比较。然而,许多研究依赖于任意的经验法则来计算样本大小,往往导致分析不足和结论不可靠。本文回顾了在基于auroc的外部验证研究中准确确定样本量的关键概念,使研究人员和临床医生更容易获得理论和实践。我们介绍了powerROC,这是一个开源的web工具,旨在简化这些计算,既可以对单个模型进行评估,也可以对两个模型进行比较。该工具提供了选择目标精度水平的指导,并采用灵活的方法,利用试验数据或用户定义的概率分布。我们通过一个使用MIMIC数据库进行医院死亡率预测的案例研究来说明powerROC的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities.

Rigorous external validation is crucial for assessing the generalizability of prediction models, particularly by evaluating their discrimination (AUROC) on new data. This often involves comparing a new model's AUROC to that of an established reference model. However, many studies rely on arbitrary rules of thumb for sample size calculations, often resulting in underpowered analyses and unreliable conclusions. This paper reviews crucial concepts for accurate sample size determination in AUROC-based external validation studies, making the theory and practice more accessible to researchers and clinicians. We introduce powerROC, an open-source web tool designed to simplify these calculations, enabling both the evaluation of a single model and the comparison of two models. The tool offers guidance on selecting target precision levels and employs flexible approaches, leveraging either pilot data or user-defined probability distributions. We illustrate powerROC's utility through a case study on hospital mortality prediction using the MIMIC database.

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