MEASUREMENT ERRORS IN EPIDEMIOLOGY--BREAST/COLON CANCER

流行病学中的测量错误——乳腺癌/结肠癌

• 批准号: 2101861
• 负责人: TOR D TOSTESON
• 金额: $ 12.36万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-05-15 至 1997-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2101861
• 关键词: breast neoplasms; colon neoplasms; computer program /software; data collection methodology /evaluation; mathematical model; neoplasm /cancer epidemiology; statistics /biometry

Errors of measurement in the risk factors under study are an important source of imprecision and bias in cancer epidemiology. An example of concern to our group is the within-person variability of questionnaire data on alcohol use being collected in a case-control study of breast cancer incidence. Recently there has been a renewed interest in studying statistical methods for treating this problem. In part, this interest can be traced to the growing use of more sophisticated techniques for determining exposures and outcomes. The new research in measurement error methods has primarily focused on techniques commonly used by epidemiologists such as binary regression and case-control studies. Yet relatively few of these methods have been introduced into the practice of cancer epidemiology. The long term objective of this application is to encourage the appropriate use of recently developed measurement-error methods. This will be accomplished by developing and documenting statistical methods in the form of functions for use in the analysis of epidemiologic data. The successful application of measurement- error methods to practical research depends on the availability of reliable implementations of the suggested algorithms in a computer software format familiar to researchers working in epidemiology. One of the recurrent themes in statistical literature regarding measurement errors in binary regression is the difficulty of doing maximum likelihood estimation when the likelihood contains an integral that cannot be evaluated analytically. A number of authors, including the investigators participating in this proposal, have suggested approximate methods of inference that eliminate this computational difficulty, including efficient score tests for generalized linear models, approximations to maximum likelihood for logistic regression, and integrable models, such as probit regression with normal measurement errors. These methods will be refined and adapted for use in studies of the etiology and prevention of breast, colon and skin cancer. A major emphasis will be placed on procedures useful for determining the appropriate sample size for epidemiologic investigations with substantial errors of measurement in risk factors.

研究中风险因素的测量误差是 癌症流行病学不精确和偏见的重要根源。一个 我们小组关注的例子是人体内的变异性 在病例对照中收集有关饮酒的问卷数据 乳腺癌发病率的研究。最近又更新了 有兴趣研究解决这个问题的统计方法。 在某种程度上，这种兴趣可以追溯到更多的使用 确定暴露和结果的复杂技术。 测量误差方法的新研究主要集中在 流行病学家常用的技术，例如二进制 回归和病例对照研究。但这些相对较少 方法已被引入癌症实践中 流行病学。 该应用程序的长期目标是鼓励 适当使用最近开发的测量误差方法。 这将通过制定和记录统计数据来实现 函数形式的方法用于分析 流行病学数据。 测量的成功应用—— 实际研究中的错误方法取决于是否有 在计算机中可靠地实现建议的算法 流行病学研究人员熟悉的软件格式。 统计文献中反复出现的主题之一是 二元回归中的测量误差是做起来的难点 最大似然估计，当似然包含 无法通过分析评估的积分。一些 作者，包括参与本研究的研究人员 提案，提出了近似的推断方法 消除这种计算困难，包括有效分数 广义线性模型的检验，最大值的近似 逻辑回归的可能性和可积模型，例如 具有正常测量误差的概率回归。 这些方法 将被完善和调整以用于病因学和 预防乳腺癌、结肠癌和皮肤癌。主要强调将 放在有助于确定适当的程序上 流行病学调查的样本量 风险因素的测量误差。