Department of Clinical Epidemiology and Biostatistics, McMaster University, Canada.
Stat Med. 2010 Apr 30;29(9):994-1003. doi: 10.1002/sim.3829. Epub 2010 Jan 19.
Poor measurement of explanatory variables occurs frequently in observational studies. Error-prone observations may lead to biased estimation and loss of power in detecting the impact of explanatory variables on the response. We consider misclassified binary exposure in the context of case-control studies, assuming the availability of validation data to inform the magnitude of the misclassification. A Bayesian adjustment to correct the misclassification is investigated. Simulation studies show that the Bayesian method can have advantages over non-Bayesian counterparts, particularly in the face of a rare exposure, small validation sample sizes, and uncertainty about whether exposure misclassification is differential or non-differential. The method is illustrated via application to several real studies.
在观察性研究中，解释性变量的测量往往不准确。有错误的观测值可能导致对解释变量对响应的影响的估计有偏差和检测能力下降。我们在病例对照研究的背景下考虑错误分类的二元暴露，假设可以使用验证数据来告知错误分类的程度。研究了一种贝叶斯调整方法来纠正错误分类。模拟研究表明，贝叶斯方法相对于非贝叶斯方法可能具有优势，特别是在面临罕见暴露、验证样本量小以及不确定暴露错误分类是差异还是非差异的情况下。该方法通过应用于几个实际研究来说明。
