Joseph Berkson (14 May 1899 – 12 September 1982) was trained as a physicist (BSc 1920 College of City of New York, M.A., 1922, Columbia), physician (M.D., 1927, Johns Hopkins), and statistician (Dr.Sc., 1928, Johns Hopkins).O'Fallon WM (1998). "Berkson, Joseph". Armitage P, Colton T, Editors-in-Chief. ''Encyclopedia of Biostatistics.'' Chichester: John Wiley & Sons. Volume 1, pp. 290-295. He is best known for having identified a source of bias in

observational studies In fields such as epidemiology, social sciences, psychology and statistics, an observational study draws inferences from a sample (statistics), sample to a statistical population, population where the dependent and independent variables, independ ...

caused by selection effects known as

Berkson's paradox Berkson's paradox, also known as Berkson's bias, collider bias, or Berkson's fallacy, is a result in conditional probability and statistics which is often found to be counterintuitive, and hence a veridical paradox. It is a complicating factor ari ...

. In 1950, as Head (1934–1964) of the Division of Biometry and Medical Statistics of the

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Rochester, Minnesota Rochester is a city in the U.S. state of Minnesota and the county seat of Olmsted County. Located on rolling bluffs on the Zumbro River's south fork in Southeast Minnesota, the city is the home and birthplace of the renowned Mayo Clinic. Acco ...

, Berkson wrote a key paper entitled ''Are there two regressions?''. In this paper Berkson proposed an error model for

regression analysis In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one ...

that contradicted the classical error model until that point assumed to generally apply and this has since been termed the

Berkson error model The Berkson error model is a description of random error (or misclassification) in measurement. Unlike classical error, Berkson error causes little or no bias in the measurement. It was proposed by Joseph Berkson in an article entitled “Are the ...

. Whereas the classical error model is statistically independent of the true variable, Berkson's model is statistically independent of the observed variable. Carroll et al. (1995) refer to the two types of error models as follows: * ''error models'' including the Classical Measurement Error models and Error Calibration Models, where the conditional distribution of ''W'' given (''Z'', ''X'') is modeled — use of such a model is appropriate when attempting to determine ''X'' directly, but this is prevented by various errors in measurement. * ''regression calibration models'' (also known as controlled-variable or Berkson error models), where the conditional distribution of ''X'' given (''Z'', ''W'') is modeled. Berkson is also widely recognised as the key proponent in the use of the logistic in preference to the

normal distribution In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is : f(x) = \frac e^ The parameter \mu ...

in probabilistic techniques.Lecture notes for Economics students at Sussex university. Online resource

/ref> Berkson is also credited with the introduction of the

logit In statistics, the logit ( ) function is the quantile function associated with the standard logistic distribution. It has many uses in data analysis and machine learning, especially in data transformations. Mathematically, the logit is the ...

model in 1944, and with coining this term. The term was borrowed by analogy from the very similar

probit In probability theory and statistics, the probit function is the quantile function associated with the standard normal distribution. It has applications in data analysis and machine learning, in particular exploratory statistical graphics and s ...

model developed by

Chester Ittner Bliss Chester Ittner Bliss (February 1, 1899 – March 14, 1979) was primarily a biologist, who is best known for his contributions to statistics. He was born in Springfield, Ohio in 1899 and died in 1979. He was the first secretary of the International ...

in 1934. Berkson was a prominent opponent of the idea that cigarette smoking causes cancer. In the 1957

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annual report, he was quoted as saying "the evidence, taken as a whole, does not establish, on any reasonable scientific basis, that cigarette smoking causes lung cancer." Following the issuance of the famous report Smoking and Health: Report of the Advisory Committee to the Surgeon General of the United States, he was quoted in

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as saying it was "very doubtful that smoking causes cancer of the lung."

Bibliography

* Berkson J. Limitations of the Application of Fourfold Table Analysis to Hospital Data. Biometrics Bulletin. 1946;2(3):47–53. PMID 21001024. * Berkson J. Limitations of the Application of Fourfold Table Analysis to Hospital Data. International Journal of Epidemiology. 2014;43(2):511–515. DOI: 10.1093/ije/dyu022. PMID 24585734. Reprint with restrictions.

Notes

{{DEFAULTSORT:Berkson, Joseph 1899 births 1982 deaths American statisticians Johns Hopkins University alumni Fellows of the American Statistical Association City College of New York alumni Columbia University alumni