Reflection on modern methods: five myths about measurement error in epidemiological research.
Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the va...
| Publicado en: | International Journal of Epidemiology Vol. 49; no. 1; pp. 338 - 348 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Feb2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142579955&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142579955 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03005771 DIH jtl: International Journal of Epidemiology issn: 03005771 maglogo: N pubinfo: dt: Feb2020 vid: 49 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 142579955 142579955 NLM31821469 142579955 10.1093/ije/dyz251 NLM31821469 142579955 ppf: 338 ppct: 10 formats: tig: atl: Reflection on modern methods: five myths about measurement error in epidemiological research. aug: au: Smeden, Maarten van Lash, Timothy L Groenwold, Rolf H H van Smeden, Maarten affil: Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands sug: subj: Epidemiological Research Models, Statistical Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Impact of Events Scale Ways of Coping Questionnaire Funding Source ab: Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study's inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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