nonzerocure_test {hdcuremodels} | R Documentation |
Non-parametric pest 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 |
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)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
km.fit <- survfit(Surv(Time, Censor) ~ 1, data = training)
nonzerocure_test(km.fit)