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

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

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

References

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

Author

Thomas Scheike

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)
#> 
#>       status   0   1   2
#> death                   
#> 0            378 167 223
#> 1            622   0   0

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)


# \donttest{
### 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"))

# }