MRC Biostatistics Unit, Cambridge, U.K.; MRC Centre for Nutritional Epidemiology in Cancer Prevention and Survival, Department of Public Health and Primary Care, University of Cambridge, Cambridge, U.K.; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, U.K.
Stat Med. 2013 Sep 30;32(22):3838-61. doi: 10.1002/sim.5803. Epub 2013 Apr 2.
Nutritional epidemiology relies largely on self-reported measures of dietary intake, errors in which give biased estimated diet-disease associations. Self-reported measurements come from questionnaires and food records. Unbiased biomarkers are scarce; however, surrogate biomarkers, which are correlated with intake but not unbiased, can also be useful. It is important to quantify and correct for the effects of measurement error on diet-disease associations. Challenges arise because there is no gold standard, and errors in self-reported measurements are correlated with true intake and each other. We describe an extended model for error in questionnaire, food record, and surrogate biomarker measurements. The focus is on estimating the degree of bias in estimated diet-disease associations due to measurement error. In particular, we propose using sensitivity analyses to assess the impact of changes in values of model parameters which are usually assumed fixed. The methods are motivated by and applied to measures of fruit and vegetable intake from questionnaires, 7-day diet diaries, and surrogate biomarker (plasma vitamin C) from over 25000 participants in the Norfolk cohort of the European Prospective Investigation into Cancer and Nutrition. Our results show that the estimated effects of error in self-reported measurements are highly sensitive to model assumptions, resulting in anything from a large attenuation to a small amplification in the diet-disease association. Commonly made assumptions could result in a large overcorrection for the effects of measurement error. Increased understanding of relationships between potential surrogate biomarkers and true dietary intake is essential for obtaining good estimates of the effects of measurement error in self-reported measurements on observed diet-disease associations.
营养流行病学在很大程度上依赖于饮食摄入的自我报告测量，其中的错误会导致饮食与疾病关联的估计产生偏差。自我报告的测量数据来自问卷和食物记录。无偏倚的生物标志物稀缺；然而，相关但不无偏的替代生物标志物也可能有用。量化和纠正测量误差对饮食与疾病关联的影响非常重要。由于没有黄金标准，且自我报告测量中的误差与真实摄入量和彼此相关，因此会出现挑战。我们描述了一种用于问卷、食物记录和替代生物标志物测量中误差的扩展模型。重点是估计由于测量误差导致的估计饮食与疾病关联中的偏差程度。特别是，我们提出使用敏感性分析来评估模型参数值变化的影响，这些参数值通常被假定为固定的。这些方法的动机和应用是基于来自诺福克队列的超过 25000 名欧洲癌症前瞻性调查和营养研究参与者的问卷、7 天饮食日记和替代生物标志物（血浆维生素 C）中水果和蔬菜摄入量的测量。我们的结果表明，自我报告测量中误差的估计效果对模型假设高度敏感，导致饮食与疾病关联的估计值从大幅衰减到小幅放大。常见的假设可能导致对测量误差影响的过度校正。增加对潜在替代生物标志物与真实饮食摄入量之间关系的理解，对于获得自我报告测量中测量误差对观察到的饮食与疾病关联的影响的良好估计至关重要。
