Plot cumulative incidence functions

Plots the nonparametric cumulative incidence of each event type over time, estimated by the Aalen–Johansen estimator (via survfit on a multi-state Surv outcome). With a single event type this equals one minus the Kaplan–Meier survival curve; with competing risks each event type gets its own curve, correctly accounting for the other event types instead of treating them as censoring. An optional group_variable draws separate curves per group (e.g. treatment arm).

Usage

cifPlot(
  data_plot_all,
  survival_variable,
  event_type_variable,
  id_variable = "id",
  censor_value = 0,
  event_labels = NULL,
  group_variable = NULL,
  conf_int = TRUE
)

Arguments

data_plot_all

A data.frame with the time-to-event outcome. It may be in long format (several rows per subject, as for the longitudinal sub-model); only the first row of each subject is used, and survival_variable, event_type_variable and group_variable must be constant within each subject.

survival_variable

Name of the time-to-event variable.

event_type_variable

Name of the variable holding each subject's event type, or censoring status.

id_variable

Name of the subject ID column. Default is "id".

censor_value

The value of event_type_variable that marks a censored subject. Default 0. Use NA when censored subjects have a missing event type, as with pbc3$status4. Every other value is treated as an event type.

event_labels

Optional legend labels for the event types: either a character vector named by the event type values (e.g. c("1" = "Death", "2" = "Transplant")), or an unnamed one in the sorted order of the event type values. Default uses the values themselves.

group_variable

Optional name of a subject-level variable to stratify the curves by, shown by line type (and also by color when there is a single event type). NULL (default) for no grouping.

conf_int

Logical; draw pointwise 95\ TRUE.

Value

A ggplot object.

Examples

data(pbc3)

# competing risks: death (1) and transplantation (2); 0 = censored
cifPlot(pbc3, survival_variable = "years", event_type_variable = "status5",
  event_labels = c("1" = "Death", "2" = "Transplant"))


# the same events coded as in status4 (censored subjects are NA)
cifPlot(pbc3, survival_variable = "years", event_type_variable = "status4",
  censor_value = NA, event_labels = c("0" = "Death", "1" = "Transplant"))


# single event type, by treatment arm
cifPlot(pbc3, survival_variable = "years", event_type_variable = "status2",
  group_variable = "drug", conf_int = FALSE)
#> Ignoring unknown labels:
#> • fill : "drug"