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Application of Quantile regression in clinical research: An overview with the help of R and SAS statistical package

Editor IJSMI


Normally the relationship between two variables x and y is studied using the linear regression equation. Linear regression equation requires normality and homoscedasticity (equal variance) assumption. When the normality and homoscedasticity assumptions are violated the linear regression estimates are not valid. Quantile regression method overcomes the drawbacks of Linear Regression and can be applied when the data is skewed and equal variance assumptions are violated. This paper provides an overview of application of quantile regression in the clinical research using R and SAS statistical package.


Quantile Regression; Linear Regression; SAS; R package

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SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.

DOI: http://dx.doi.org/10.3000/ijsmi.v2i1.5

DOI (PDF): http://dx.doi.org/10.3000/ijsmi.v2i1.5.g14

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