LRfit: Estimate the while-alive loss (event) rate

In WA: While-Alive Loss Rate for Recurrent Event in the Presence of Death

Description

Estimate and make inference on the while-alive loss (or event) rate across J groups under a user-specified loss function

Usage

LRfit(id, time, status, trt, Dweight = 0, wH = NULL, wD = NULL)

Arguments

id	A vector of id variable.
time	A vector of follow-up times.
status	A vector of event type variable; 1 = recurrent event, 2 = death, and 0 = censoring.
trt	A vector of categorical (binary or multiclass) variable for treatment group.
Dweight	A non-negative weight for death relative to the recurrent event; Default is 0.
wH	A function of t and m to weight recurrent event; t: time; m: existing number of recurrent event; Default is the constant function of 1.
wD	A function of t and m to weight death; t: time; m: existing number of recurrent event; Default is the constant function of 0.

Value

An object of class LRfit. See LRfit.object for details.

See Also

LRfit.object, summary.LRfit, plot.LRfit.

Examples

# load the HF-ACTION trial data
head(hfaction_cpx12)
# fit the data
dat<-hfaction_cpx12
obj<-LRfit(dat$id,dat$time,dat$status,dat$trt)
# print the event numbers by group
obj
# summarize the inference results for tau=3.5 years
# with joint test with RMST
summary(obj,tau=3.5,joint.test=TRUE)
# plot the estimated survival-completed cumulative loss
# by group, with 95% confidence intervals
plot(obj,conf=TRUE,xlab="Time (years)",xlim=c(0, 3.5),ylim=c(0,3),
    ylab="Survival-completed cumulative frequency")

WA documentation built on Nov. 18, 2021, 1:10 a.m.