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. 2017 Sep 19;17(1):146.
doi: 10.1186/s12874-017-0421-6.

Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology

Derrick A Bennett  1 , Denise Landry  2 , Julian Little  2 , Cosetta Minelli  3
Affiliations

Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology

Derrick A Bennett et al. BMC Med Res Methodol. .

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Continuous exposure; Measurement error; Nutrition; Statistical methods.

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The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
Screening and selection of included studies
Fig. 2
Fig. 2
Summary of the main approaches used by studies included in the systematic review. a Approaches to correct for measurement error. b Approaches to quantify measurement error. FREQ.: Represents the number of studies that reported using the particular approach
See this image and copyright information in PMC

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