Zhang Li, Mukherjee Bhramar, Ghosh Malay, Gruber Stephen, Moreno Victor
Department of Quantitative Health Sciences, The Cleveland Clinic Foundation, Cleveland, OH-44195, USA.
Stat Med. 2008 Jul 10;27(15):2756-83. doi: 10.1002/sim.3044.
We consider analysis of data from an unmatched case-control study design with a binary genetic factor and a binary environmental exposure when both genetic and environmental exposures could be potentially misclassified. We devise an estimation strategy that corrects for misclassification errors and also exploits the gene-environment independence assumption. The proposed corrected point estimates and confidence intervals for misclassified data reduce back to standard analytical forms as the misclassification error rates go to zero. We illustrate the methods by simulating unmatched case-control data sets under varying levels of disease-exposure association and with different degrees of misclassification. A real data set on a case-control study of colorectal cancer where a validation subsample is available for assessing genotyping error is used to illustrate our methods.
当遗传暴露和环境暴露都可能被错误分类时，我们考虑对来自具有二元遗传因素和二元环境暴露的非匹配病例对照研究设计的数据进行分析。我们设计了一种估计策略，该策略可校正错误分类误差，并利用基因-环境独立性假设。随着错误分类误差率趋于零，针对错误分类数据提出的校正点估计和置信区间可简化为标准分析形式。我们通过在不同疾病-暴露关联水平和不同错误分类程度下模拟非匹配病例对照数据集来说明这些方法。一个关于结直肠癌病例对照研究的真实数据集被用来阐述我们的方法，该数据集中有一个验证子样本可用于评估基因分型误差。
