Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology.
Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology.
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DOI：
10.1186/s12874-017-0421-6
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发表时间：
2017-09-19
影响因子：
4
通讯作者：
Minelli C
中科院分区：
文献类型：
--
作者：
Bennett DA;Landry D;Little J;Minelli C
已经提出了几种统计方法来评估和纠正暴露测量误差。我们的目的是提供营养流行病学中最常用方法的关键概述。检索MEDLINE、EMBASE、BIOSIS和CINAHL截至2016年5月发表的英文报告，以确定研究描述了使用校准研究量化和/或纠正营养流行病学连续暴露测量误差的方法。我们确定了126项研究，其中43项描述了统计方法，83项将这些方法中的任何一种应用于实际数据集。在符合条件的研究中，统计方法分为：a)量化不同膳食评估工具与“真实摄入量”之间关系的方法，主要基于相关分析和三元分析法；B)调整饮食-疾病关联测量误差的点和区间估计值的方法，主要基于回归校准分析及其扩展。提出了处理差分测量误差的两种方法（多次插值和矩重构）。回归校准是营养流行病学中最常用的校正测量误差的方法，确保其假设和要求得到充分满足至关重要。研究偏离经典测量误差模型对回归校准估计的影响的分析可以帮助研究人员解释他们的发现。当回归校正不合适时，可能使用替代方法，方法的选择应取决于假设的测量误差模型、合适的校准研究数据的可用性以及由于违反经典测量误差模型假设而产生偏差的可能性。在此综述的基础上，我们对营养流行病学测量误差评估和校正方法的使用提出了一些实用的建议。本文的在线版本（10.1186/s12874-017-0421-6）包含补充资料，仅供授权用户使用。
Several statistical approaches have been proposed to assess and correct for exposure measurement error. We aimed to provide a critical overview of the most common approaches used in nutritional epidemiology. MEDLINE, EMBASE, BIOSIS and CINAHL were searched for reports published in English up to May 2016 in order to ascertain studies that described methods aimed to quantify and/or correct for measurement error for a continuous exposure in nutritional epidemiology using a calibration study. We identified 126 studies, 43 of which described statistical methods and 83 that applied any of these methods to a real dataset. The statistical approaches in the eligible studies were grouped into: a) approaches to quantify the relationship between different dietary assessment instruments and “true intake”, which were mostly based on correlation analysis and the method of triads; b) approaches to adjust point and interval estimates of diet-disease associations for measurement error, mostly based on regression calibration analysis and its extensions. Two approaches (multiple imputation and moment reconstruction) were identified that can deal with differential measurement error. For regression calibration, the most common approach to correct for measurement error used in nutritional epidemiology, it is crucial to ensure that its assumptions and requirements are fully met. Analyses that investigate the impact of departures from the classical measurement error model on regression calibration estimates can be helpful to researchers in interpreting their findings. With regard to the possible use of alternative methods when regression calibration is not appropriate, the choice of method should depend on the measurement error model assumed, the availability of suitable calibration study data and the potential for bias due to violation of the classical measurement error model assumptions. On the basis of this review, we provide some practical advice for the use of methods to assess and adjust for measurement error in nutritional epidemiology. The online version of this article (10.1186/s12874-017-0421-6) contains supplementary material, which is available to authorized users.
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影响因子：
2
作者：
Buonaccorsi, John P.;Dalen, Ingvild;Thoresen, Magne
通讯作者：
Thoresen, Magne
影响因子：
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Beydoun, May A.;Kaufman, Jay S.;Heiss, Gerardo
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作者：
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通讯作者：
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作者：
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通讯作者：
Cruz, JA