Log-Linear Modeling of Agreement among Expert Exposure Assessors.
Log-Linear Modeling of Agreement among Expert Exposure Assessors.
复制标题
专家暴露评估员之间协议的对数线性模型。
DOI：
10.1093/annhyg/mev011
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Milton,Donald
中科院分区：
文献类型：
--
作者：
Hunt,PhillipR;Friesen,MelissaC;Sama,Susan;Ryan,Louise;Milton,Donald
在缺乏有效测量的情况下，对专家评估暴露的评价取决于专家评估者之间的一致性。一致性通常使用Cohen的Kappa统计来衡量，然而，这种方法有一些众所周知的局限性。我们展示了一种替代方法，使用对数线性模型设计的模型协议。这些模型包含区分精确一致性（一致性矩阵的对角线）和非精确关联性（非对角线）的参数。此外，他们可以将协变量，以检查是否协议不同strature.MethodsWe应用这些模型来评估协议之间的专家评级暴露于致敏物（无，可能，高）在职业性asthma.ResultsTraditional分析使用加权Kappa建议潜在的差异，协议的蓝领/白色领工作和办公室/非办公室工作，但不情况/对照状态。然而，在对数线性模型中对协变量及其相互作用项的评价未发现与这些协变量的一致性存在差异，并提供证据证明使用kappa观察到的差异是评级分布的边际差异而不是一致性差异的结果。在协议的差异预测整个暴露规模，与可能的中度暴露类别更难区分的专家高度暴露的类别比从unexposed category.ConclusionsThe对数线性模型提供了有价值的信息模式的协议和结构的数据，没有透露在分析中使用Kappa。该模型不依赖于边缘分布，并且易于评估协变量，因此可以可靠地检测暴露数据中的观察偏倚。
BackgroundEvaluation of expert assessment of exposure depends, in the absence of a validation measurement, upon measures of agreement among the expert raters. Agreement is typically measured using Cohen’s Kappa statistic, however, there are some well-known limitations to this approach. We demonstrate an alternate method that uses log-linear models designed to model agreement. These models contain parameters that distinguish between exact agreement (diagonals of agreement matrix) and non-exact associations (off-diagonals). In addition, they can incorporate covariates to examine whether agreement differs across strata.MethodsWe applied these models to evaluate agreement among expert ratings of exposure to sensitizers (none, likely, high) in a study of occupational asthma.ResultsTraditional analyses using weighted kappa suggested potential differences in agreement by blue/white collar jobs and office/non-office jobs, but not case/control status. However, the evaluation of the covariates and their interaction terms in log-linear models found no differences in agreement with these covariates and provided evidence that the differences observed using kappa were the result of marginal differences in the distribution of ratings rather than differences in agreement. Differences in agreement were predicted across the exposure scale, with the likely moderately exposed category more difficult for the experts to differentiate from the highly exposed category than from the unexposed category.ConclusionsThe log-linear models provided valuable information about patterns of agreement and the structure of the data that were not revealed in analyses using kappa. The models’ lack of dependence on marginal distributions and the ease of evaluating covariates allow reliable detection of observational bias in exposure data.
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