Abstract
BackgroundSeveral 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.MethodsMEDLINE, 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.ResultsWe 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.ConclusionsFor 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.
Highlights
Several statistical approaches have been proposed to assess and correct for exposure measurement error
We used a narrative approach for data synthesis and broadly classified the primary statistical approaches used in the 126 studies into two groups: a) approaches to quantify or assess the relationship between different dietary assessment instruments and “true intake”; b) approaches to adjust point and interval estimates of dietdisease associations for measurement error (Fig. 2)
Approaches to quantify the relationship between different dietary assessment instruments and “true intake” The dietary assessment instrument used most often in large-scale epidemiological studies is the Food Frequency Questionnaire (FFQ), which suffers from random measurement errors
Summary
Several statistical approaches have been proposed to assess and correct for exposure measurement error. Many exposures investigated in epidemiological research, such as physical activity, air pollution and dietary intake, are challenging to measure and prone to measurement error. Measurement error in nutritional epidemiology Two general types of measurement error are described in the nutritional epidemiological literature: random errors and systematic errors. Random errors are chance fluctuations or random variations in dietary intake that average out to the truth in the long run if many repeats are taken (i.e. the law of large numbers applies) [5]. Systematic errors are more serious as they do not average out to the true value even when a large number of repeats are taken.
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