Guolo A, Brazzale A R
Department of Statistics, University of Padova, Via Cesare Battisti, 241, I-35121 Padova, Italy.
Stat Med. 2008 Aug 30;27(19):3755-75. doi: 10.1002/sim.3282.
The presence of measurement errors affecting the covariates in regression models is a relevant topic in many scientific areas, as, for example, in epidemiology. An example is given by an epidemiological population-based matched case-control study on the aetiology of childhood malignancies, which is currently under completion in Italy. This study was aimed at evaluating the effects of childhood exposure to extremely low electromagnetic fields on the risk of disease occurrence by taking into account the possibility of erroneous measures of the exposure. Within this framework, we focus on the application of likelihood methods to correct for measurement error. This approach, which has received less attention in literature with respect to alternatives, is compared with commonly used methods such as regression calibration and SIMEX. The comparison is performed by simulation, under a broad range of measurement error structures.
回归模型中影响协变量的测量误差的存在是许多科学领域中的一个相关主题，例如在流行病学中。一个例子是意大利目前正在进行的一项基于人群的儿童恶性肿瘤病因学匹配病例对照研究。这项研究旨在通过考虑暴露测量错误的可能性，评估儿童暴露于极低电磁场对疾病发生风险的影响。在此框架内，我们专注于应用似然方法来校正测量误差。与回归校准和SIMEX等常用方法相比，这种在文献中受到关注较少的方法在广泛的测量误差结构下通过模拟进行了比较。
