Statistical primer: multivariable regression considerations and pitfalls† | Zendy

Stuart W Grant | Zendy; Graeme L. Hickey | Zendy; Stuart J. Head | Zendy

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Statistical primer: multivariable regression considerations and pitfalls†

Author(s) -

Stuart W Grant,

Graeme L. Hickey,

Stuart J. Head

Publication year - 2018

Publication title -

european journal of cardio-thoracic surgery

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 1.303

H-Index - 133

eISSN - 1873-734X

pISSN - 1010-7940

DOI - 10.1093/ejcts/ezy403

Subject(s) - multivariable calculus , logistic regression , regression analysis , regression diagnostic , regression , linear regression , statistics , computer science , econometrics , mathematics , polynomial regression , engineering , control engineering

Multivariable regression models are used to establish the relationship between a dependent variable (i.e. an outcome of interest) and more than 1 independent variable. Multivariable regression can be used for a variety of different purposes in research studies. The 3 most common types of multivariable regression are linear regression, logistic regression and Cox proportional hazards regression. A detailed understanding of multivariable regression is essential for correct interpretation of studies that utilize these statistical tools. This statistical primer discusses some common considerations and pitfalls for researchers to be aware of when undertaking multivariable regression.

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