巨像:弥合大数据和辐射流行病学之间的鸿沟。

IF 1.8 4区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES
Eric Giunta, Benjamin French, Linda Walsh, Lawrence T Dauer, John D Boice, Daniel Andresen, Amir Bahadori
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引用次数: 0

摘要

需要使用大数据集来适应复杂模型的软件来回答辐射流行病学中持续存在的和新出现的问题。开源R包Colossus就是为了满足这种需求而开发的。Colossus旨在利用R脚本的输入和绘图灵活性,采用多核系统来更快地运行分析,并允许直接添加未来的功能。百万人口研究使用巨像进行分析,估计指数风险比和线性超额相对风险,包括Wald置信边界和基于可能性的边界。将协变量不确定性传播到模型参数不确定性的方法是下一个主要关注的领域。此外,正在实施分段剂量反应模型、梯度下降算法选项和其他统计测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Colossus: Bridging the gap between big data and radiation epidemiology.

Software to fit complex models using big data sets is needed to answer persistent and emerging questions in radiation epidemiology. The open-source R package Colossus has been developed to meet this need. Colossus was designed to take advantage of the input and graphing flexibility of R scripts, employ multi-core systems to run analyses faster, and permit the straightforward addition of future capabilities. The Million Person Study has used Colossus for analyses that estimate exponential hazard ratios and linear excess relative risks, both with Wald confidence boundaries and likelihood-based boundaries. Incorporating methods to propagate covariate uncertainty into model parameter uncertainty is the next major focus area. In addition, piecewise dose-response models, gradient descent algorithm options, and other statistical tests are being implemented.

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来源期刊
Journal of Radiological Protection
Journal of Radiological Protection 环境科学-公共卫生、环境卫生与职业卫生
CiteScore
2.60
自引率
26.70%
发文量
137
审稿时长
18-36 weeks
期刊介绍: Journal of Radiological Protection publishes articles on all aspects of radiological protection, including non-ionising as well as ionising radiations. Fields of interest range from research, development and theory to operational matters, education and training. The very wide spectrum of its topics includes: dosimetry, instrument development, specialized measuring techniques, epidemiology, biological effects (in vivo and in vitro) and risk and environmental impact assessments. The journal encourages publication of data and code as well as results.
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