Estimation of covariance for bivariate recurrent events with terminal event
Estimation of probability of more that k events for recurrent events process where there is terminal event
covarianceRecurrent( data, type1, type2, status = "status", death = "death", start = "start", stop = "stop", id = "id", names.count = "Count" )
data |
data-frame |
type1 |
type of first event (code) related to status |
type2 |
type of second event (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 |
names.count |
name of count for number of previous event of different types, here generated by count.history() |
Thomas Scheike
Scheike, Eriksson, Tribler (2019) The mean, variance and correlation for bivariate recurrent events with a terminal event, JRSS-C
########################################
## getting some data to work on
########################################
data(base1cumhaz)
data(base4cumhaz)
data(drcumhaz)
dr <- drcumhaz
base1 <- base1cumhaz
base4 <- base4cumhaz
rr <- simRecurrent(1000,base1,cumhaz2=base4,death.cumhaz=dr)
rr <- count.history(rr)
rr$strata <- 1
dtable(rr,~death+status)
covrp <- covarianceRecurrent(rr,1,2,status="status",death="death",
start="entry",stop="time",id="id",names.count="Count")
par(mfrow=c(1,3))
plot(covrp)
### with strata, each strata in matrix column, provides basis for fast Bootstrap
covrpS <- covarianceRecurrentS(rr,1,2,status="status",death="death",
start="entry",stop="time",strata="strata",id="id",names.count="Count")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.