Reflection on modern methods: demystifying robust standard errors for epidemiologists.
All statistical estimates from data have uncertainty due to sampling variability. A standard error is one measure of uncertainty of a sample estimate (such as the mean of a set of observations or a regression coefficient). Standard errors are usually calculated based on assumptions underpinning the...
| Publicado en: | International Journal of Epidemiology Vol. 50; no. 1; pp. 346 - 352 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Oxford University Press / USA
Feb2021
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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=149178070&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149178070 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03005771 DIH jtl: International Journal of Epidemiology issn: 03005771 maglogo: N pubinfo: dt: Feb2021 vid: 50 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 149178070 149178070 NLM33351919 10.1093/ije/dyaa260 NLM33351919 149178070 ppf: 346 ppct: 6 formats: tig: atl: Reflection on modern methods: demystifying robust standard errors for epidemiologists. aug: au: Mansournia, Mohammad Ali Nazemipour, Maryam Naimi, Ashley I Collins, Gary S Campbell, Michael J affil: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences , Tehran, Iran sug: subj: Models, Statistical Cluster Analysis Uncertainty Scales ab: All statistical estimates from data have uncertainty due to sampling variability. A standard error is one measure of uncertainty of a sample estimate (such as the mean of a set of observations or a regression coefficient). Standard errors are usually calculated based on assumptions underpinning the statistical model used in the estimation. However, there are situations in which some assumptions of the statistical model including the variance or covariance of the outcome across observations are violated, which leads to biased standard errors. One simple remedy is to use robust standard errors, which are robust to violations of certain assumptions of the statistical model. Robust standard errors are frequently used in clinical papers (e.g. to account for clustering of observations), although the underlying concepts behind robust standard errors and when to use them are often not well understood. In this paper, we demystify robust standard errors using several worked examples in simple situations in which model assumptions involving the variance or covariance of the outcome are misspecified. These are: (i) when the observed variances are different, (ii) when the variance specified in the model is wrong and (iii) when the assumption of independence is wrong. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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