Quantile regression in the field of liver transplantation: A case study-based tutorial.

Abstract

We present a tutorial on quantile regression, an underutilized yet valuable class of multivariable linear regression models that allows researchers to understand more fully the conditional distribution of response as compared to models based on conditional means. Quantile regression models are flexible, have attractive interpretations, and are implemented in most statistical software packages. Our focus is on an intuitive understanding of quantile regression models, particularly as compared with more familiar regression methods such as conditional mean models as estimated using ordinary least squares (OLS). We frame our tutorial through 2 clinical case studies in the field of liver transplantation: one in the context of estimating the recipient's financial burden after transplantation and another in estimating intraoperative blood transfusion needs. Our real-world cases demonstrate how quantile regression models give researchers a richer understanding of relationships in the data and provide a more nuanced clinical understanding compared to more commonly used linear regression models. We encourage researchers to explore quantile regression as a tool to answer relevant clinical research questions and support their more widespread adoption.