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癌症死亡率的跨国趋势。方法学问题与结果。

Multinational trends in cancer mortality rates. Methodological issues and results.

作者信息

Schwartz J

机构信息

Office of Policy Analysis, U.S. Environmental Protection Agency, Washington, D.C. 20460.

出版信息

Ann N Y Acad Sci. 1990;609:136-45. doi: 10.1111/j.1749-6632.1990.tb32062.x.

Abstract

Identification of trends in cancer mortality rates for specific sites is useful both for targeting areas of health concern and for suggesting topics where epidemiologic research into etiology might be fruitful. Differences and similarities across countries may provide clues for further research. Counts of cancer mortality by sites are often considered as Poisson processes. Using this framework, Poisson regressions can be used to identify time trends. Model identification can also be difficult and is aided by the graphical techniques of exploratory data analysis. Plots of the data can identify the existence of trends, nonlinearities, cohorts with special risks, and artifacts due to such things as ICD revision. Because human perception is often distorted by outliers in the data, local nonparametric smoothing can yield more robust plots for model identification. Quasi-likelihood methods can be used to incorporate serially correlated and overdispersed covariance structures. These techniques are illustrated using cancer mortality data from selected industrialized countries.

摘要

确定特定部位癌症死亡率的趋势,对于确定健康关注领域以及为病因学的流行病学研究可能富有成果的主题提供建议都很有用。各国之间的差异和相似之处可能为进一步研究提供线索。按部位划分的癌症死亡人数通常被视为泊松过程。使用这个框架,泊松回归可用于识别时间趋势。模型识别也可能很困难,探索性数据分析的图形技术有助于进行模型识别。数据图可以识别趋势的存在、非线性、具有特殊风险的队列以及由于国际疾病分类(ICD)修订等因素导致的伪像。由于人类的感知常常被数据中的异常值扭曲,局部非参数平滑可以生成更稳健的图用于模型识别。拟似然方法可用于纳入序列相关和过度分散的协方差结构。使用选定工业化国家的癌症死亡率数据对这些技术进行了说明。

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