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

split_data(
  formula,
  data,
  cut = NULL,
  max_time = NULL,
  multiple_id = FALSE,
  ...
)

Arguments

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. The left-hand-side can be in start-stop notation. This, however, is only used to create left-truncated data and does not support the full functionality.
data: Either an object inheriting from data frame or in case of time-dependent covariates a list of data frames (of length 2), 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: Split points, used to partition the follow-up into intervals. If unspecified, all unique event times will be used. For competing risks, when combine = TRUE split points are derived from all event types combined.
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.
multiple_id: Are occurences of same id allowed (per transition). Defaults to FALSE, but is sometimes set to TRUE, e.g., in case of multi-state models with back transitions.
...: Further arguments passed to the data.frame method and eventually to survSplit.