Skewed Slash Censored Quantile Regression

In survival studies, the response variable is the time to desired event, which usually has skewness and censoring. For this reason, its mean modeling does not provide a complete picture of the density function. The quantile regression model investigates the effect of covariates in different percentiles by modeling duration time. The skewed slash distribution, having an addition parameter ( $$\upsilon $$ υ ), can change the tail width of the density function by changing its value, and is a more flexible distribution than other asymmetric distributions. We considered the skewed slash quantile regression model for survival data that interpret the effect of covariates on time-to-event. Likelihood-based approach and Nelder-Mead algorithm were used to fit the model. An application to data from AML patients receiving allo-HCT was presented to illustrate the theory and method developed in this paper. The Skewed slash distribution, by matching skewed data sets with heavy tails (Setting with the $$\varvec{\nu }$$ ν parameter), is useful for analyzing skewed and heavy tail data sets.