Quantile Regression and Its Applications: A Primer for Anesthesiologists

Multivariable regression analysis is a powerful statistical tool in biomedical research with numerous applications. While linear regression can be used to model the expected value (ie, mean) of a continuous outcome given the covariates in the model, quantile regression can be used to compare the entire distribution of a continuous response or a specific quantile of the response between groups. The advantage of the quantile regression methodology is that it allows for understanding relationships between variables outside of the conditional mean of the response; it is useful for understanding an outcome at its various quantiles and comparing groups or levels of an exposure on those quantiles. We present quantile regression in a 3-step approach: determining that quantile regression is desired, fitting the quantile regression model, and interpreting the model results. We then apply our quantile regression analysis approach using 2 illustrative examples from the 2015 American College of Surgeons National Surgical Quality Improvement Program Pediatric database, and 1 example utilizing data on duration of sensory block in rats. Accepted for publication December 12, 2018. Funding: None. The authors declare no conflicts of interest. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://bit.ly/KegmMq). Reprints will not be available from the authors. Address correspondence to Steven J. Staffa, MS, Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115. Address e-mail to steven.staffa@childrens.harvard.edu. © 2019 International Anesthesia Research Society

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