testmz: Test of Maller-Zhou
In npcure: Nonparametric Estimation in Mixture Cure Models

Description Usage Arguments Details Value Author(s) References See Also Examples

This function carries out the nonparametric test of Maller and Zhou (1992).

1	testmz(t, d, dataset)

`t`	If `dataset` is missing, a numeric object giving the the observed times. If `dataset` is a data frame, it is interpreted as the name of the variable corresponding to the observed times in the data frame.
`d`	If `dataset` is missing, an integer object giving the values of the uncensoring indicator. Censored observations must be coded as 0, uncensored ones as 1. If `dataset` is a data frame, it is interpreted as the name of the variable corresponding to the uncensoring indicator.
`dataset`	An optional data frame in which the variables named in `x`, `t` and `d` are interpreted. If it is missing, `x`, `t` and `d` must be objects of the workspace.

The function implements Maller and Zhou's (1992) method to test the null hypothesis H_0 : tau_F_0 > tau_G vs. H_1 : tau_F_0 <= tau_G, where tau_F_0 and tau_G are the supports of, respectively, the distribution function of the survival time of the uncured and the distribution function of the censoring time.

An object of S3 class 'npcure'. Formally, a list of components:

`type`	The constant character string c("test", "Maller-Zhou").
`pvalue`	The p-value of the test.
`aux`	A list of components: `statistic`, the test statistic, `n` the sample size, `delta`, the difference between the largest observed time t_n and the largest uncensored time t_n^, and `interval`, a vector giving the range between max(0, t_n^* - delta)* and t_n^*.

Ignacio López-de-Ullibarri [aut, cre], Ana López-Cheda [aut], Maria Amalia Jácome [aut]

Maller R. A., Zhou S. (1992). Estimating the proportion of immunes in a censored sample. Biometrika, 79: 731-739. https://doi.org/10.1093/biomet/79.4.731.

latency, probcure

## Some artificial data
set.seed(123)
n <- 50
y <- qweibull(runif(n)*pweibull(2, shape = 2), shape = 2) ## True lifetimes
c <- qexp(runif(n)*pexp(2.5)) ## Censoring values
u <- runif(n)
## Probability of being susceptible is constantly equal to .5
t <- ifelse(u < .5, pmin(y, c), c) ## Observed times
d <- ifelse(u < .5, ifelse(y < c, 1, 0), 0) ## Uncensoring indicator
data <- data.frame(t = t, d = d)

## Maller-Zhou test
testmz(t, d, data)