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冠状动脉风险预测的再校准:七国研究示例。

Re-calibration of coronary risk prediction: an example of the Seven Countries Study.

机构信息

Department of Cardiovascular, Respiratory, Nephrological, Anesthesiological and Geriatric Sciences, Sapienza University of Rome, Rome, Italy.

Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands and Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

出版信息

Sci Rep. 2017 Dec 14;7(1):17552. doi: 10.1038/s41598-017-17784-2.

Abstract

We aimed at performing a calibration and re-calibration process using six standard risk factors from Northern (NE, N = 2360) or Southern European (SE, N = 2789) middle-aged men of the Seven Countries Study, whose parameters and data were fully known, to establish whether re-calibration gave the right answer. Greenwood-Nam-D'Agostino technique as modified by Demler (GNDD) in 2015 produced chi-squared statistics using 10 deciles of observed/expected CHD mortality risk, corresponding to Hosmer-Lemeshaw chi-squared employed for multiple logistic equations whereby binary data are used. Instead of the number of events, the GNDD test uses survival probabilities of observed and predicted events. The exercise applied, in five different ways, the parameters of the NE-predictive model to SE (and vice-versa) and compared the outcome of the simulated re-calibration with the real data. Good re-calibration could be obtained only when risk factor coefficients were substituted, being similar in magnitude and not significantly different between NE-SE. In all other ways, a good re-calibration could not be obtained. This is enough to praise for an overall need of re-evaluation of most investigations that, without GNDD or another proper technique for statistically assessing the potential differences, concluded that re-calibration is a fair method and might therefore be used, with no specific caution.

摘要

我们旨在使用来自北欧(NE,N=2360)或南欧(SE,N=2789)中年男性的六项标准风险因素进行校准和重新校准过程,这些男性的参数和数据是完全已知的,以确定重新校准是否给出了正确答案。Greenwood-Nam-D'Agostino 技术(GNDD)由 Demler 在 2015 年进行了修改,使用观察到的/预期的 CHD 死亡率的 10 个十分位数产生卡方统计量,这与 Hosmer-Lemeshaw 卡方用于多逻辑方程相同,其中使用了二进制数据。GNDD 测试不是使用事件数量,而是使用观察到的和预测的事件的生存概率。该研究以五种不同方式将 NE 预测模型的参数应用于 SE(反之亦然),并将模拟重新校准的结果与实际数据进行比较。只有当风险因素系数被替换时,才能获得良好的重新校准,这些系数在大小上相似,并且在 NE-SE 之间没有显著差异。在所有其他情况下,都无法获得良好的重新校准。这足以说明需要重新评估大多数研究,这些研究没有 GNDD 或其他用于统计评估潜在差异的适当技术,得出重新校准是一种公平的方法,因此可以使用,而无需特别谨慎。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a7/5730554/ae47bb34bcb5/41598_2017_17784_Fig1_HTML.jpg

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