Lyles Robert H, Allen Andrew S, Dana Flanders W, Kupper Lawrence L, Christensen Deborah L
Department of Biostatistics, The Rollins School of Public Health of Emory University, Atlanta, GA 30322, USA.
Stat Med. 2006 Dec 15;25(23):4065-80. doi: 10.1002/sim.2500.
In case-control studies, it is common for a categorical exposure variable to be misclassified. It is also common for exposure status to be informatively missing for some individuals, in that the probability of missingness may be related to exposure. Procedures for addressing the bias due to misclassification via validation data have been extensively studied, and related methods have been proposed for dealing with informative missingness based on supplemental sampling of some of those with missing data. In this paper, we introduce study designs and analytic procedures for dealing with both problems simultaneously in a 2x2 analysis. Results based on convergence in probability illustrate that the combined effects of missingness and misclassification, even when the latter is non-differential, can lead to naïve exposure odds ratio estimates that are inflated or on the wrong side of the null. The motivating example comes from a case-control study of the association between low birth weight and the diagnosis of breast cancer later in life, where self-reported birth weight for some women is supplemented by accurate information from birth certificates.
在病例对照研究中，分类暴露变量被误分类的情况很常见。对于一些个体而言，暴露状态信息缺失的情况也很常见，因为缺失的概率可能与暴露有关。通过验证数据解决误分类导致的偏差的程序已得到广泛研究，并且已经提出了基于对一些缺失数据个体的补充抽样来处理信息性缺失的相关方法。在本文中，我们介绍了在2×2分析中同时处理这两个问题的研究设计和分析程序。基于概率收敛的结果表明，即使后者是非差异性的，缺失和误分类的综合影响也可能导致朴素暴露比值比估计值被夸大或处于零假设的错误一侧。激发该研究的例子来自一项关于低出生体重与晚年乳腺癌诊断之间关联的病例对照研究，其中一些女性的自我报告出生体重由出生证明中的准确信息补充。
