MEASUREMENT ERRORS IN EPIDEMIOLOGY--BREAST/COLON CANCER

研究中的风险因素的测量误差是一个 癌症流行病学中不精确和偏差的重要来源。一个 我们小组关注的一个例子是人内的变异性， 在病例对照组中收集关于酒精使用的问卷数据 乳腺癌发病率的研究。最近，有一个新的 对研究处理这个问题的统计方法感兴趣。 在某种程度上，这种兴趣可以追溯到越来越多地使用更多的 确定风险和结果的复杂技术。 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，如二进制 回归分析和病例对照研究。然而，其中相对较少的 方法已经被引入到癌症的实践中， 流行病学 这项申请的长远目标是鼓励 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- 错误的方法，以实际研究取决于可用性， 在计算机中可靠地实现所建议的算法 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一个主题是， 二元回归中的测量误差是一个难点， 最大似然估计，当可能性包含 不能用解析法计算的积分。一些 作者，包括参与这项研究的研究人员， 建议，提出了近似的推理方法， 消除这种计算困难，包括有效的分数 广义线性模型的检验，最大值的近似 逻辑回归的可能性，以及可积模型，例如 具有正常测量误差的概率单位回归。 这些方法 将被完善和调整，用于病因学研究， 预防乳腺癌、结肠癌和皮肤癌。一个主要的重点将是 应置于有助于确定适当的 流行病学调查的样本量 风险因素的测量误差。

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.