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摘要翻译：
为了评估疾病风险预测工具的校准，通常计算量$E/O$，即预期事件数与观察事件数的比率。然而，由于审查，或更确切地说，由于在研究结束前辍学的个人，这一数量通常无法用于完整的人口研究，必须计算替代估计数。在本文中，我们提出并比较了四种实现这一目标的方法。我们表明两个最常用的方法一般导致有偏估计。我们的论点首先基于一些理论上的考虑。然后，我们进行了一个模拟研究，以突出前面提到的偏差的大小。作为一个结论性的例子，我们评估了现有的乳腺癌预测模型在E3N-EPIC队列中的校准。
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英文标题：
《How to evaluate the calibration of a disease risk prediction tool》
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作者：
V. Viallon, J. Benichou, F. Clavel-Chapelon and S. Ragusa
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
To evaluate the calibration of a disease risk prediction tool, the quantity $E/O$, i.e., the ratio of the expected number of events to the observed number of events, is generally computed. However, because of censoring, or more precisely because of individuals who drop out before the termination of the study, this quantity is generally unavailable for the complete population study and an alternative estimate has to be computed. In this paper, we present and compare four methods to do this. We show that two of the most commonly used methods generally lead to biased estimates. Our arguments are first based on some theoretic considerations. Then, we perform a simulation study to highlight the magnitude of the previously mentioned biases. As a concluding example, we evaluate the calibration of an existing predictive model for breast cancer on the E3N-EPIC cohort.
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PDF链接：
https://arxiv.org/pdf/710.5268
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