Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology
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 pub...
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| Format: | Article |
| Language: | English |
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BMC
2017-09-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | http://link.springer.com/article/10.1186/s12874-017-0421-6 |
| _version_ | 1818236888023040000 |
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| author | Derrick A. Bennett Denise Landry Julian Little Cosetta Minelli |
| author_facet | Derrick A. Bennett Denise Landry Julian Little Cosetta Minelli |
| author_sort | Derrick A. Bennett |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T12:17:01Z |
| format | Article |
| id | doaj.art-fba6499a253d443fa4a285834402babd |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-12-12T12:17:01Z |
| publishDate | 2017-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-fba6499a253d443fa4a285834402babd2022-12-22T00:24:45ZengBMCBMC Medical Research Methodology1471-22882017-09-0117112210.1186/s12874-017-0421-6Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiologyDerrick A. Bennett0Denise Landry1Julian Little2Cosetta Minelli3Nuffield Department of Population Health, University of Oxford, Big Data InstituteSchool of Epidemiology, Public Health and Preventive Medicine, University of OttawaSchool of Epidemiology, Public Health and Preventive Medicine, University of OttawaPopulation Health & Occupational Disease, National Heart and Lung Institute, Imperial College LondonAbstract 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.http://link.springer.com/article/10.1186/s12874-017-0421-6Measurement errorContinuous exposureNutritionStatistical methods |
| spellingShingle | Derrick A. Bennett Denise Landry Julian Little Cosetta Minelli Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology BMC Medical Research Methodology Measurement error Continuous exposure Nutrition Statistical methods |
| title | Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology |
| title_full | Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology |
| title_fullStr | Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology |
| title_full_unstemmed | Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology |
| title_short | Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology |
| title_sort | systematic review of statistical approaches to quantify or correct for measurement error in a continuous exposure in nutritional epidemiology |
| topic | Measurement error Continuous exposure Nutrition Statistical methods |
| url | http://link.springer.com/article/10.1186/s12874-017-0421-6 |
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