SingleArmAUC: Single Arm Area Under the Cumulative Count Curve

In zrmacc/MCC: Functions for Comparing Mean Cumulative Count Curves

Single Arm Area Under the Cumulative Count Curve

Description

Single Arm Area Under the Cumulative Count Curve

Usage

SingleArmAUC(
  data,
  alpha = 0.05,
  boot = FALSE,
  cens_after_last = TRUE,
  idx_name = "idx",
  reps = 2000,
  status_name = "status",
  strata = NULL,
  tau = NULL,
  time_name = "time",
  weights = NULL
)

Arguments

data	Data.frame.
alpha	Type I error level.
boot	Logical, construct bootstrap confidence intervals (slow)?
cens_after_last	Should subjects who lack an explicit censoring time be censored after their last observed event?
idx_name	Name of column containing a unique subject index.
reps	Number of replicates for bootstrap inference.
status_name	Name of column containing the status. Must be coded as 0 for censoring, 1 for event, 2 for death. Each subject should have an observation-terminating event, either censoring or death.
strata	Optional stratification factor.
tau	Numeric truncation time.
time_name	Name of column containing the observation time.
weights	Optional column of weights, controlling the size of the jump in the cumulative count curve at times with status == 1.

Value

Object of class compAUCs with these slots:

• '@Areas': The AUC for each arm.

• '@CIs': Observed difference and ratio in areas with confidence intervals.

• '@Curves': Mean cumulative count curve for each arm; averaged across strata if present.

• '@Pvals': Bootstrap and permutation p-values.

• '@Reps': Bootstrap and permutation realizations of the test statistics.

• '@Weights': Per-stratum weights and AUCs.

Examples

# Simulate data set.
covar <- data.frame(strata = rep(c(1, 2), each = 50))
data <- GenData(beta_event = log(0.5), covariates = covar)
# Calculate AUC.
auc <- SingleArmAUC(data, strata = data$strata, tau = 2)
auc <- SingleArmAUC(data, boot = TRUE, reps = 100, strata = data$strata, tau = 2)
show(auc)

zrmacc/MCC documentation built on July 16, 2025, 4:04 p.m.