contstructs start stop formulation of event time data after a variable in the data.set. Similar to SurvSplit of the survival package but can also split after random time given in data frame.
event.split(
data,
time = "time",
status = "status",
cuts = "cuts",
name.id = "id",
name.start = "start",
cens.code = 0,
order.id = TRUE,
time.group = FALSE
)data to be split
time variable.
status variable.
cuts variable or numeric cut (only one value)
name of id variable.
name of start variable in data, start can also be numeric "0"
code for the censoring.
order data after id and start.
make variable "before"."cut" that keeps track of wether start,stop is before (1) or after cut (0).
set.seed(1)
d <- data.frame(event=round(5*runif(5),2),start=1:5,time=2*1:5,
status=rbinom(5,1,0.5),x=1:5)
d
#> event start time status x
#> 1 1.33 1 2 1 1
#> 2 1.86 2 4 1 2
#> 3 2.86 3 6 1 3
#> 4 4.54 4 8 1 4
#> 5 1.01 5 10 0 5
d0 <- event.split(d,cuts="event",name.start=0)
d0
#> event start time status x start.0 id
#> 1 1.33 1 1.33 0 1 0.00 1
#> 1.1 1.33 1 2.00 1 1 1.33 1
#> 2 1.86 2 1.86 0 2 0.00 2
#> 2.1 1.86 2 4.00 1 2 1.86 2
#> 3 2.86 3 2.86 0 3 0.00 3
#> 3.1 2.86 3 6.00 1 3 2.86 3
#> 4 4.54 4 4.54 0 4 0.00 4
#> 4.1 4.54 4 8.00 1 4 4.54 4
#> 5 1.01 5 1.01 0 5 0.00 5
#> 5.1 1.01 5 10.00 0 5 1.01 5
dd <- event.split(d,cuts="event")
dd
#> event start time status x id
#> 1 1.33 1.00 1.33 0 1 1
#> 1.1 1.33 1.33 2.00 1 1 1
#> 2 1.86 2.00 4.00 1 2 2
#> 3 2.86 3.00 6.00 1 3 3
#> 4 4.54 4.00 4.54 0 4 4
#> 4.1 4.54 4.54 8.00 1 4 4
#> 5 1.01 5.00 10.00 0 5 5
ddd <- event.split(dd,cuts=3.5)
ddd
#> event start time status x id cut.3.5
#> 1 1.33 1.00 1.33 0 1 1 3.5
#> 1.1 1.33 1.33 2.00 1 1 1 3.5
#> 2 1.86 2.00 3.50 0 2 2 3.5
#> 2.1 1.86 3.50 4.00 1 2 2 3.5
#> 3 2.86 3.00 3.50 0 3 3 3.5
#> 3.1 2.86 3.50 6.00 1 3 3 3.5
#> 4 4.54 4.00 4.54 0 4 4 3.5
#> 4.1 4.54 4.54 8.00 1 4 4 3.5
#> 5 1.01 5.00 10.00 0 5 5 3.5
event.split(ddd,cuts=5.5)
#> event start time status x id cut.3.5 cut.5.5
#> 1 1.33 1.00 1.33 0 1 1 3.5 5.5
#> 1.1 1.33 1.33 2.00 1 1 1 3.5 5.5
#> 2 1.86 2.00 3.50 0 2 2 3.5 5.5
#> 2.1 1.86 3.50 4.00 1 2 2 3.5 5.5
#> 3 2.86 3.00 3.50 0 3 3 3.5 5.5
#> 3.1 2.86 3.50 5.50 0 3 3 3.5 5.5
#> 3.1.1 2.86 5.50 6.00 1 3 3 3.5 5.5
#> 4 4.54 4.00 4.54 0 4 4 3.5 5.5
#> 4.1 4.54 4.54 5.50 0 4 4 3.5 5.5
#> 4.1.1 4.54 5.50 8.00 1 4 4 3.5 5.5
#> 5 1.01 5.00 5.50 0 5 5 3.5 5.5
#> 5.1 1.01 5.50 10.00 0 5 5 3.5 5.5
### successive cutting for many values
dd <- d
for (cuts in seq(2,3,by=0.3)) dd <- event.split(dd,cuts=cuts)
dd
#> event start time status x cut.2 id cut.2.3 cut.2.6 cut.2.9
#> 1 1.33 1.0 2.0 1 1 2 1 2.3 2.6 2.9
#> 2 1.86 2.0 2.3 0 2 2 2 2.3 2.6 2.9
#> 2.1 1.86 2.3 2.6 0 2 2 2 2.3 2.6 2.9
#> 2.1.1 1.86 2.6 2.9 0 2 2 2 2.3 2.6 2.9
#> 2.1.1.1 1.86 2.9 4.0 1 2 2 2 2.3 2.6 2.9
#> 3 2.86 3.0 6.0 1 3 2 3 2.3 2.6 2.9
#> 4 4.54 4.0 8.0 1 4 2 4 2.3 2.6 2.9
#> 5 1.01 5.0 10.0 0 5 2 5 2.3 2.6 2.9
###########################################################################
### same but for situation with multiple events along the time-axis
###########################################################################
d <- data.frame(event1=1:5+runif(5)*0.5,start=1:5,time=2*1:5,
status=rbinom(5,1,0.5),x=1:5,start0=0)
d$event2 <- d$event1+0.2
d$event2[4:5] <- NA
d
#> event1 start time status x start0 event2
#> 1 1.102987 1 2 0 1 0 1.302987
#> 2 2.088278 2 4 1 2 0 2.288278
#> 3 3.343511 3 6 1 3 0 3.543511
#> 4 4.192052 4 8 0 4 0 NA
#> 5 5.384921 5 10 1 5 0 NA
d0 <- event.split(d,cuts="event1",name.start="start",time="time",status="status")
d0
#> event1 start time status x start0 event2 id
#> 1 1.102987 1.000000 1.102987 0 1 0 1.302987 1
#> 1.1 1.102987 1.102987 2.000000 0 1 0 1.302987 1
#> 2 2.088278 2.000000 2.088278 0 2 0 2.288278 2
#> 2.1 2.088278 2.088278 4.000000 1 2 0 2.288278 2
#> 3 3.343511 3.000000 3.343511 0 3 0 3.543511 3
#> 3.1 3.343511 3.343511 6.000000 1 3 0 3.543511 3
#> 4 4.192052 4.000000 4.192052 0 4 0 NA 4
#> 4.1 4.192052 4.192052 8.000000 0 4 0 NA 4
#> 5 5.384921 5.000000 5.384921 0 5 0 NA 5
#> 5.1 5.384921 5.384921 10.000000 1 5 0 NA 5
###
d00 <- event.split(d0,cuts="event2",name.start="start",time="time",status="status")
d00
#> event1 start time status x start0 event2 id
#> 1 1.102987 1.000000 1.102987 0 1 0 1.302987 1
#> 1.1 1.102987 1.102987 1.302987 0 1 0 1.302987 1
#> 1.1.1 1.102987 1.302987 2.000000 0 1 0 1.302987 1
#> 2 2.088278 2.000000 2.088278 0 2 0 2.288278 2
#> 2.1 2.088278 2.088278 2.288278 0 2 0 2.288278 2
#> 2.1.1 2.088278 2.288278 4.000000 1 2 0 2.288278 2
#> 3 3.343511 3.000000 3.343511 0 3 0 3.543511 3
#> 3.1 3.343511 3.343511 3.543511 0 3 0 3.543511 3
#> 3.1.1 3.343511 3.543511 6.000000 1 3 0 3.543511 3
#> 4 4.192052 4.000000 4.192052 0 4 0 NA 4
#> 4.1 4.192052 4.192052 8.000000 0 4 0 NA 4
#> 5 5.384921 5.000000 5.384921 0 5 0 NA 5
#> 5.1 5.384921 5.384921 10.000000 1 5 0 NA 5