Calculate the Kaplan-Meier estimator and the Aalen-Johansen estimator

Core estimation routine that computes a survfit-compatible object from a formula + data interface (Event() or survival::Surv() on the LHS, and a stratification variable on the RHS if necessary). The back-end C++ routine supports both weighted and stratified data. Use this when you want numbers only (e.g. estimates, SEs, CIs and influence functions) and will plot it yourself.

Usage

cifcurve(
  formula,
  data,
  weights = NULL,
  subset.condition = NULL,
  na.action = na.omit,
  outcome.type = c("survival", "competing-risk"),
  code.event1 = 1,
  code.event2 = 2,
  code.censoring = 0,
  error = NULL,
  conf.type = "arcsine-square root",
  conf.int = 0.95,
  report.influence.function = FALSE,
  report.survfit.std.err = FALSE,
  engine = "calculateAJ_Rcpp",
  prob.bound = 1e-07
)

Arguments

formula

A model formula specifying the time-to-event outcome on the LHS (typically Event(time, status) or survival::Surv(time, status)) and, optionally, a stratification variable on the RHS. Unlike cifplot(), this function does not accept a fitted survfit object.

data

A data frame containing variables in the formula.

weights

Optional name of the weight variable in data. Weights must be nonnegative.

subset.condition

Optional character string giving a logical condition to subset data (default NULL).

na.action

A function specifying the action to take on missing values (default na.omit).

outcome.type

Character string specifying the type of time-to-event outcome. One of "survival" (Kaplan-Meier) or "competing-risk" (Aalen-Johansen). If NULL (default), the function automatically infers the outcome type from the data: if the event variable has more than two unique levels, "competing-risk" is assumed; otherwise, "survival" is used. You can also use abbreviations such as "S" or "C". Mixed or ambiguous inputs (e.g., c("S", "C")) trigger automatic detection based on the event coding.

code.event1

Integer code of the event of interest (default 1).

code.event2

Integer code of the competing risk (default 2).

code.censoring

Integer code of censoring (default 0).

error

Character string specifying the method for SEs and CIs used internally. For "survival" without weights, choose one of "greenwood" (default), "tsiatis", or "if". For "competing-risk" without weights, choose one of "delta" (default), "aalen", or "if". SEs and CIs based on influence functions ("if") is recommended for weighted analysis.

conf.type

Character specifying the method of transformation for CIs used internally (default arcsine-square root).

conf.int

Numeric two-sided level of CIs (default 0.95).

report.influence.function

Logical. When TRUE and engine = "calculateAJ_Rcpp", the influence function is also computed and returned (default FALSE).

report.survfit.std.err

Logical. If TRUE, report SE on the log-survival scale (survfit's convention). Otherwise SE is on the probability scale.

engine

Character. One of "auto", "calculateKM", or "calculateAJ_Rcpp" (default "calculateAJ_Rcpp").

prob.bound

Numeric lower bound used to internally truncate probabilities away from 0 and 1 (default 1e-7).

Value

A "survfit" object. For outcome.type="survival", $surv is the survival function. For outcome.type="competing-risk", $surv equals 1 - CIF for code.event1. SE and CIs are provided per error, conf.type and conf.int. This enables an independent use of standard methods for survfit such as:

• summary(): time-by-time estimates with SEs and CIs

• plot(): base R stepwise survival/CIF curves

• mean(): restricted mean survival estimates with CIs

• quantile(): quantile estimates with CIs

Note that some methods (e.g. residuals.survfit) may not be supported.

Details

Typical use cases

• When outcome.type = "survival", this is a thin wrapper around the KM estimator with the chosen variance / CI transformation.

• When outcome.type = "competing-risk", this computes the AJ estimator of CIF for code.event1. The returned $surv is 1 - CIF, i.e. in the format that ggsurvfit expects.

• Use cifplot() if you want to go straight to a figure; use cifcurve() if you only want the numbers.

Standard error and confidence intervals

Argument	Description	Default
error	SE for KM: "greenwood", "tsiatis", "if". For CIF: "aalen", "delta", "if".	"greenwood", "delta" or "if"
conf.type	Transformation for CIs: "plain", "log", "log-log", "arcsin", "logit", or "none".	"arcsin"
conf.int	Two-sided CI level.	0.95

Lifecycle

See also

polyreg() for log-odds product modeling of CIFs; cifplot() for display of a CIF; cifpanel() for display of multiple CIFs; ggsurvfit::ggsurvfit, patchwork::patchwork and modelsummary::modelsummary for display helpers.

Examples

data(diabetes.complications)
output1 <- cifcurve(Event(t,epsilon) ~ fruitq,
                    data = diabetes.complications,
                    outcome.type="competing-risk")
cifplot(output1,
        outcome.type = "competing-risk",
        type.y = "risk",
        add.risktable = FALSE,
        label.y = "CIF of diabetic retinopathy",
        label.x = "Years from registration")