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 ( \(\upsilon \) ), 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 \(\varvec{\nu }\) parameter), is useful for analyzing skewed and heavy tail data sets.
