nonzerocure_test: Non-parametric test for a non-zero cured fraction

In hdcuremodels: High-Dimensional Cure Models

Non-parametric test for a non-zero cured fraction

Description

Tests the null hypothesis that the proportion of observations susceptible to the event = 1 against the alternative that the proportion of observations susceptible to the event is < 1. If the null hypothesis is rejected, there is a significant cured fraction.

Usage

nonzerocure_test(object, reps = 1000, seed = NULL, plot = FALSE, b = NULL)

Arguments

object	a survfit object.
reps	number of simulations on which to base the p-value (default = 1000).
seed	optional random seed.
plot	logical. If TRUE a histogram of the estimated susceptible proportions over all simulations is produced.
b	optional. If specified the maximum observed time for the uniform distribution for generating the censoring times. If not specified, an exponential model is used for generating the censoring times (default).

Value

proportion_susceptible	estimated proportion of susceptibles
proportion_cured	estimated proportion of those cured
p_value	p-value testing the null hypothesis that the proportion of susceptibles = 1 (cured fraction = 0) against the alternative that the proportion of susceptibles < 1 (non-zero cured fraction)
time_95_percent_of_events	estimated time at which 95% of events should have occurred

References

Maller, R. A. and Zhou, X. (1996) Survival Analysis with Long-Term Survivors. John Wiley & Sons.

See Also

survfit, cure_estimate, sufficient_fu_test

Examples

library(survival)
withr::local_seed(1234)
temp <- generate_cure_data(n = 100, j = 10, n_true = 10, a = 1.8)
training <- temp$training
km_fit <- survfit(Surv(Time, Censor) ~ 1, data = training)
nonzerocure_test(km_fit)

hdcuremodels documentation built on Aug. 8, 2025, 7:38 p.m.