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




Analysis of Variance, Post-hoc, Post hoc, multiple comparison, ANOVA, SAS, R package


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.


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How to Cite

IJSMI, E. (2017). Application of Quantile regression in clinical research: An overview with the help of R and SAS statistical package. International Journal of Statistics and Medical Informatics, 2(1), 1-6.