split_data: Function to transform data without time-dependent covariates...
In adibender/ped: What the Package Does (One Line, Title Case)

Description Usage Arguments See Also Examples

Function to transform data without time-dependent covariates into piece-wise exponential data format

1	split_data(formula, data, cut = NULL, max_time = NULL, ...)

`formula`	A two sided formula with a `Surv` object on the left-hand-side and covariate specification on the right-hand-side (RHS). The RHS can be an extended formula, which specifies how TDCs should be transformed using specials `concurrent` and `cumulative`.
`data`	Either an object inheriting from data frame or in case of time-dependent covariates a list of data frames, where the first data frame contains the time-to-event information and static covariates while the second (and potentially further data frames) contain information on time-dependent covariates and the times at which they have been observed.
`cut`	Break points, used to partition the follow up into intervals. If unspecified, all unique event times will be used.
`max_time`	If `cut` is unspecified, this will be the last possible event time. All event times after `max_time` will be administratively censored at `max_time`.
`...`	Further arguments passed to the `data.frame` method and eventually to `survSplit`

survSplit

data("veteran", package="survival")
head(veteran)
ped <- split_data(Surv(time, status)~trt + age, data=veteran,
   cut=c(0, 100, 400), id="id")
head(ped)
class(ped) # class ped (piece-wise exponential data)