# Berkson error model

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The '''Berkson error model''' is a description of [random error](/source/random_error) (or misclassification) in [measurement](/source/measurement). Unlike [classical error](/source/classical_error), Berkson error causes little or no bias in the measurement.  It was proposed by [Joseph Berkson](/source/Joseph_Berkson) in an article entitled “Are there two regressions?,”<ref name="berkson">{{cite journal |last=Berkson |first=J. |title=Are There Two Regressions? |journal=[Journal of the American Statistical Association](/source/Journal_of_the_American_Statistical_Association) |volume=45 |issue=250 |year=1950 |pages=164–180 |jstor=2280676 |doi=10.1080/01621459.1950.10483349}}</ref> published in 1950.

An example of Berkson error arises in [exposure assessment](/source/exposure_assessment) in epidemiological studies. Berkson error may predominate over [classical error](/source/classical_error) in cases where exposure data are highly aggregated. While this kind of error reduces the [power](/source/statistical_power) of a study, risk estimates themselves are not themselves attenuated (as would be the case where [random error](/source/random_error) predominates).

==References==
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==Further reading==
* {{cite book |last=Buonaccorsi |first=John P. |title=Measurement Error: Models, Methods, and Applications |publisher=CRC Press |year=2010 |isbn=978-1-4200-6658-6 |pages=76–78 |url=https://books.google.com/books?id=QVtVmaCqLHMC&pg=PA76 }}
* {{cite book |last=Carroll |first=R. J. |last2=Ruppert |first2=D. |last3=Stefanski |first3=L. A. |title=Measurement Error in Nonlinear Models |publisher=Chapman & Hall |location=London |edition=Second |year=2006 |isbn=1-4200-1013-1 |pages=26–32 |url=https://books.google.com/books?id=9kBx5CPZCqkC&pg=PA26 }}

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Category:Accuracy and precision
Category:Statistical deviation and dispersion
Category:Errors and residuals

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