QRegIT: QRegIT function

In yicjia/QRegIT: Quantile regression on Inactivity time

Description

QRegIT is an R package for quantile (Q) regression (Reg) on inactivity (I) time (T). The inactivity time can be interpreted as time lost due to an event of interest, reversed lifetime, or time beyond an adverse event such as transition to an addictive drug. We propose a quantile regression model to associate the inactivity time with potential predictors, adjusting for confounding factors.

Usage

QRegIT(time, event, cov, t0, tau, type = 1, nPerturb = 400)

Arguments

time	the follow up time
event	the status indicator, normally 0 = censored
cov	the covariate(s) used in the regression
t0	a pre-specified time point used to define inactivity time. t0 can be chosen from the observation period whose maximum would be the administrative censoring.
tau	the desired quantile; this is a number strictly between 0 and 1.
type	the event of primary interest when competing risks are present. When there are no competing events, the default is type = 1.
nPerturb	the number of perturbations; default = 400

Value

QRegIT returns a data frame contianing estimated coefficients and 95 percent confidence interval.

Author(s)

Yichen Jia <[e-mail]>, Jong-Hyeon Jeong <[e-mail]>.

References

Yichen Jia, Jong-Hyeon Jeong. Cause-Specific Quantile Regression on Inactivity Time. Submitted 2019.

Examples

# Load the example data testdata
data("testdata")
# Test run of QRegIT:
cov = testdata[,c("x1","x2","x3")]
testRun = QRegIT(testdata$time, testdata$event, cov, t0 = 15, tau = 0.5, type = 1, nPerturb=1000)
# End

yicjia/QRegIT documentation built on Aug. 6, 2021, 3:56 a.m.