Sensitivity analysis applied to epidemiology

产品说明： 拟议的出版物将收集和综合的方法，量化系统误差，影响非随机流行病学研究。传统的置信区间在非随机流行病学研究中作为总不确定性的度量表现不佳，因为它们只考虑随机误差，但这些研究中的大多数误差来自系统性来源。流行病学研究产生效果估计（如风险比），其结果对卫生干预的所有方面都有影响。伴随这些效应估计的误差被定义为它们与真实效应的差异，并且可以分为随机误差和系统误差。随着研究规模的增加，随机误差接近于零，但系统误差不会。精确度量化效应估计中的随机误差量，通常用置信区间表示。有效性衡量系统误差的数量，但很少量化。与效应估计相关的系统误差的定量评估可以通过敏感性分析进行。 拟议出版物的主要读者包括非随机流行病学研究的所有作者，分析师和消费者。该出版物将指导那些计划进行敏感性分析的读者，并将说明在进行分析和总结结果以供出版时可应用于数据集的敏感性分析方法。该出版物将包括一个文本部分和一个软件部分。该文本将作为一本常规书籍和电子版在国家生物技术信息中心在线书架上出版。该软件将在BUSPH互联网上免费提供。软件文件将是Microsoft Excel文件和SAS宏代码，用于说明方法，并允许读者使用其数据实施灵敏度分析方法。该出版物将提高研究者评估效应估计中系统误差的能力。

DESCRIPTION: The proposed publication will collect and synthesize methods for quantifying systematic errors that affect non-randomized epidemiologic research. Conventional confidence intervals perform poorly as measures of total uncertainty in non-randomized epidemiologic research because they account for only random error, yet the majority of the error in these studies arises from systematic sources. Epidemiologic studies yield effect estimates (such as the risk ratio) and their results have an impact on all aspects of health interventions. The error accompanying these effect estimates is defined as their difference from the true effects, and can be divided into random error and systematic error. The random error approaches zero as the study size increases but systematic error does not. Precision quantifies the amount of random error in an effect estimate, and is usually represented by a confidence interval. Validity measures the amount of systematic error, but is seldom quantified. A quantitative assessment of systematic error associated with an effect estimate can be made by sensitivity analysis. The primary audience of the proposed publication comprises all authors, analysts, and consumers of non-randomized epidemiologic research. The publication will guide readers who are planning for sensitivity analyses and will illustrate methods of sensitivity analysis that can be applied to data sets as they conduct analyses and summarize results for publication. The publication will have a text component and a software component. The text will be published as a conventional book and electronically at the National Center for Biotechnology Information on-line bookshelf. The software will be freely available on the BUSPH internet. The software files will be Microsoft Excel files and SAS macrocode that illustrate the methods and allow the reader to implement sensitivity analysis methods with their data. This publication will enhance the ability of investigators to assess systematic error in effect estimates.