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当暴露被错误分类时,用于估计暴露-疾病关系比值比的验证研究设计。

Design of validation studies for estimating the odds ratio of exposure-disease relationships when exposure is misclassified.

作者信息

Holcroft C A, Spiegelman D

机构信息

Department of Work Environment, University of Massachusetts Lowell, 01854, USA.

出版信息

Biometrics. 1999 Dec;55(4):1193-201. doi: 10.1111/j.0006-341x.1999.01193.x.

Abstract

We compared several validation study designs for estimating the odds ratio of disease with misclassified exposure. We assumed that the outcome and misclassified binary covariate are available and that the error-free binary covariate is measured in a subsample, the validation sample. We considered designs in which the total size of the validation sample is fixed and the probability of selection into the validation sample may depend on outcome and misclassified covariate values. Design comparisons were conducted for rare and common disease scenarios, where the optimal design is the one that minimizes the variance of the maximum likelihood estimator of the true log odds ratio relating the outcome to the exposure of interest. Misclassification rates were assumed to be independent of the outcome. We used a sensitivity analysis to assess the effect of misspecifying the misclassification rates. Under the scenarios considered, our results suggested that a balanced design, which allocates equal numbers of validation subjects into each of the four outcome/mismeasured covariate categories, is preferable for its simplicity and good performance. A user-friendly Fortran program is available from the second author, which calculates the optimal sampling fractions for all designs considered and the efficiencies of these designs relative to the optimal hybrid design for any scenario of interest.

摘要

我们比较了几种用于估计疾病与暴露误分类情况下比值比的验证研究设计。我们假定结局和误分类的二元协变量是可得的,且无误差的二元协变量在一个子样本(验证样本)中进行测量。我们考虑了验证样本总规模固定且入选验证样本的概率可能取决于结局和误分类协变量值的设计。针对罕见病和常见疾病场景进行了设计比较,其中最优设计是使将结局与感兴趣的暴露相关的真实对数比值比的最大似然估计量的方差最小化的设计。假定误分类率与结局无关。我们使用敏感性分析来评估误设误分类率的影响。在所考虑的场景下,我们的结果表明,一种平衡设计,即将相等数量的验证对象分配到四个结局/误测量协变量类别中的每一个类别,因其简单性和良好性能而更可取。第二作者提供了一个用户友好的Fortran程序,该程序可计算所考虑的所有设计的最优抽样比例以及这些设计相对于任何感兴趣场景下的最优混合设计的效率。

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