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"
)

Arguments

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()

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 data to work on 
########################################
data(base1cumhaz)
data(base4cumhaz)
data(drcumhaz)
dr <- drcumhaz
base1 <- base1cumhaz
base4 <- base4cumhaz
rr <- simRecurrentII(1000,base1,cumhaz2=base4,death.cumhaz=dr)
rr <- count.history(rr)
rr$strata <- 1
dtable(rr,~death+status)
#> 
#>       status    0    1    2
#> death                      
#> 0             154 2998  447
#> 1             846    0    0

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")