Let T be a dependent variable, such as a survival time, and let x be a p 1 covariate vector. Quantile regression methods focus on modeling the conditional quantile function,
, which is defined as
For example, is the conditional median quantile, and
is the conditional quantile function that corresponds to the 95th percentile.
A linear quantile regression model for has the form
. One of the advantages of quantile regression analysis is that the covariate effect
can change with
. Unlike ordinary least squares regression, which estimates the conditional expectation function
, quantile regression offers the flexibility to model the entire conditional distribution.
Given observations , standard quantile regression estimates the regression coefficients
by minimizing the following objective function over b:
where
However, in many applications, the responses are subject to censoring. For example, in a biomedical study, censoring occurs when patients withdraw from the study or die
from a cause that is unrelated to the disease being studied.
Let denote the censoring variable. In the case of right-censoring, the triples
are observed, where
and
are the observed response variable and the censoring indicator, respectively. Standard quantile regression can lead to a
biased estimator of the regression parameters
when censoring occurs.
The following sections describe two methods for estimating the quantile coefficient in the presence of right-censoring.