ub.xmlui.mirage2.page-structure.muninLogoub.xmlui.mirage2.page-structure.openResearchArchiveLogo
    • EnglishEnglish
    • norsknorsk
  • Velg spraaknorsk 
    • EnglishEnglish
    • norsknorsk
  • Administrasjon/UB
Vis innførsel 
  •   Hjem
  • Det helsevitenskapelige fakultet
  • Institutt for samfunnsmedisin
  • Artikler, rapporter og annet (samfunnsmedisin)
  • Vis innførsel
  •   Hjem
  • Det helsevitenskapelige fakultet
  • Institutt for samfunnsmedisin
  • Artikler, rapporter og annet (samfunnsmedisin)
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

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

Permanent lenke
https://hdl.handle.net/10037/10358
DOI
https://doi.org/10.1186/s12874-016-0240-1
Thumbnail
Åpne
article.pdf (533.5Kb)
(PDF)
Dato
2016-10-13
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Forfatter
Agogo, George O.; van der Voet, Hilko; Van 'T Veer, Pieter; Ferrari, Pietro; Muller, David; Sánchez-Cantalejo, Emilio; Bamia, Christina; Braaten, Tonje; Knüppel, Sven; Johansson, Ingegerd; Van Eeuwijk, Fred A.; Boshuizen, Hendriek C.
Sammendrag
Background:
Measurement 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.
Methods:
We 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 literaturereported 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.
Results:
Using 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.
Conclusions:
The 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.
Keywords:
Attenuation-contamination matrix, Bayesian MCMC, EPIC study, Measurement error, Validation study
Beskrivelse
Source: doi: 10.1186/s12874-016-0240-1
Forlag
BioMed Central
Sitering
Agogo GO, van der Voet H, Van 'T Veer P, Ferrari P, Muller D, Sánchez-Cantalejo E, Bamia C, Braaten T, Knüppel S, Johansson I, Van Eeuwijk FA, Boshuizen HC. A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data. BMC Medical Research Methodology. 2016;16:139
Metadata
Vis full innførsel
Samlinger
  • Artikler, rapporter og annet (samfunnsmedisin) [1515]

Bla

Bla i hele MuninEnheter og samlingerForfatterlisteTittelDatoBla i denne samlingenForfatterlisteTittelDato
Logg inn

Statistikk

Antall visninger
UiT

Munin bygger på DSpace

UiT Norges Arktiske Universitet
Universitetsbiblioteket
uit.no/ub - munin@ub.uit.no

Tilgjengelighetserklæring