prob.exceed.recurrent: Estimation of probability of more that k events for recurrent...

In mets: Analysis of Multivariate Event Times

Estimation of probability of more that k events for recurrent events process

Description

Estimation of probability of more that k events for recurrent events process where there is terminal event, based on this also estimate of variance of recurrent events. The estimator is based on cumulative incidence of exceeding "k" events. In contrast the probability of exceeding k events can also be computed as a counting process integral, and this is implemented in prob.exceedRecurrent

Usage

prob.exceed.recurrent(
  data,
  type,
  status = "status",
  death = "death",
  start = "start",
  stop = "stop",
  id = "id",
  times = NULL,
  exceed = NULL,
  cifmets = TRUE,
  strata = NULL,
  all.cifs = FALSE,
  ...
)

Arguments

data	data-frame
type	type of evnent (code) related to status
status	name of status
death	name of death indicator
start	start stop call of Hist() of prodlim
stop	start stop call of Hist() of prodlim
id	id
times	time at which to get probabilites P(N1(t) >= n)
exceed	n's for which which to compute probabilites P(N1(t) >= n)
cifmets	if true uses cif of mets package rather than prodlim
strata	to stratify according to variable, only for cifmets=TRUE, when strata is given then only consider the output in the all.cifs
all.cifs	if true then returns list of all fitted objects in cif.exceed
...	Additional arguments to lower level funtions

Author(s)

Thomas Scheike

References

Scheike, Eriksson, Tribler (2019) The mean, variance and correlation for bivariate recurrent events with a terminal event, JRSS-C

Examples

########################################
## getting some rates to mimick 
########################################

data(base1cumhaz)
data(base4cumhaz)
data(drcumhaz)
dr <- drcumhaz
base1 <- base1cumhaz
base4 <- base4cumhaz

cor.mat <- corM <- rbind(c(1.0, 0.6, 0.9), c(0.6, 1.0, 0.5), c(0.9, 0.5, 1.0))
rr <- simRecurrentII(1000,base4,cumhaz2=base4,death.cumhaz=dr,cens=2/5000)
rr <-  count.history(rr)
dtable(rr,~death+status)

oo <- prob.exceedRecurrent(rr,1)
bplot(oo)

par(mfrow=c(1,2))
with(oo,plot(time,mu,col=2,type="l"))
###
with(oo,plot(time,varN,type="l"))


### Bivariate probability of exceeding 
oo <- prob.exceedBiRecurrent(rr,1,2,exceed1=c(1,5),exceed2=c(1,2))
with(oo, matplot(time,pe1e2,type="s"))
nc <- ncol(oo$pe1e2)
legend("topleft",legend=colnames(oo$pe1e2),lty=1:nc,col=1:nc)



### do not test to avoid dependence on prodlim 
### now estimation based on cumualative incidence, but do not test to avoid dependence on prodlim 
### library(prodlim)
pp <- prob.exceed.recurrent(rr,1,status="status",death="death",start="entry",stop="time",id="id")
with(pp, matplot(times,prob,type="s"))
###
with(pp, matlines(times,se.lower,type="s"))
with(pp, matlines(times,se.upper,type="s"))

mets documentation built on Jan. 17, 2023, 5:12 p.m.