Evidence map›Paper›PMID 27737637›Full record

ArticleBMC medical research methodology2016

A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data.

George O Agogo, Hilko van der Voet, Pieter van 't Veer, Pietro Ferrari, David C Muller, Emilio Sánchez-Cantalejo, Christina Bamia, Tonje Braaten, Sven Knüppel, Ingegerd Johansson and 2 more

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Article in BMC medical research methodology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

George O AgogoBiometris, Wageningen University and Research Centre, Wageningen, The Netherlands. george.agogo@yale.edu.
Hilko van der VoetBiometris, Wageningen University and Research Centre, Wageningen, The Netherlands.
Pieter van 't VeerDepartment of Human Nutrition, Wageningen University and Research Centre, Wageningen, The Netherlands.
Pietro FerrariNutritional Epidemiology Group, International Agency for Research on Cancer, Lyon, France.
David C MullerGenetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France.
Emilio Sánchez-CantalejoAndalusian School of Public Health, Granada, Spain.
Christina BamiaDepartment of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece.
Tonje BraatenDepartment of Community Medicine, University of Tromsø, N-9037, Tromsø, Norway.
Sven KnüppelDepartment of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany.
Ingegerd JohanssonDepartment of Odontology, Umeå university, Umeå, Sweden.
Fred A van EeuwijkBiometris, Wageningen University and Research Centre, Wageningen, The Netherlands.
Hendriek C BoshuizenDepartment of Statistics, mathematical modelling and data logistics, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundMeasurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data.

methodsWe proposed a method to adjust for the bias in the diet-disease association (hereafter, association), due to measurement error in dietary intake and a mismeasured confounder, when there is no internal validation data. The method combines prior information on the validity of the self-report instrument with the observed data to adjust for the bias in the association. We compared the proposed method with the method that ignores the confounder effect, and with the method that ignores measurement errors completely. We assessed the sensitivity of the estimates to various magnitudes of measurement error, error correlations and uncertainty in the literature-reported validation data. We applied the methods to fruits and vegetables (FV) intakes, cigarette smoking (confounder) and all-cause mortality data from the European Prospective Investigation into Cancer and Nutrition study.

resultsUsing the proposed method resulted in about four times increase in the strength of association between FV intake and mortality. For weakly correlated errors, measurement error in the confounder minimally affected the hazard ratio estimate for FV intake. The effect was more pronounced for strong error correlations.

conclusionsThe proposed method permits sensitivity analysis on measurement error structures and accounts for uncertainties in the reported validity coefficients. The method is useful in assessing the direction and quantifying the magnitude of bias in the association due to measurement errors in the confounders.

Indexed as

BiasDietHumansMulticenter Studies as TopicMultivariate AnalysisNeoplasmsProportional Hazards ModelsProspective StudiesRisk AssessmentSelf ReportSensitivity and SpecificitySmokingValidation Studies as TopicAttenuation-contamination matrixBayesian MCMCEPIC studyMeasurement errorValidation study

Identifiers

PMID27737637
PMCPMC5064985

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.