nonzerocure_test: 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)

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

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).

References

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

Examples

Run this code

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)