perm_mvlogrank: Multivariate permutation logrank test
In MultSurvTests: Permutation Tests for Multivariate Survival Analysis

Description Usage Arguments Details Value References Examples

View source: R/finished_permutations.R

Computes the p-value of the multivariate permutation logrank test described in Persson et al. (2019).

1	perm_mvlogrank(B, z, delta.z, n1)

`B`	An integer specifying the number of permutations to perform. The default is 999. It is recommended to use `choose_B` for choosing `B`.
`z`	A matrix containing the observed (possibly censored) survival times for the two groups. The observations for the first group should be one the first `n1` rows.
`delta.z`	A matrix containing the censoring status of each observation in `z`.
`n1`	An integer specifying the sample size of the first group.

Multivariate version of the logrank and Gehan tests were described by Wei & Lachin (1984). Persson et al. (2019) described permutation versions of these tests, with improved performance.

A p-value.

\insertRef

persson19MultSurvTests

\insertRef

wei84MultSurvTests

# Diabetes data:
?diabetes
# Survival times for the two groups:
x <- as.matrix(subset(diabetes, LASER==1)[c(6,8)])
y <- as.matrix(subset(diabetes, LASER==2)[c(6,8)])
# Censoring status for the two groups:
delta.x <- as.matrix(subset(diabetes, LASER==1)[c(7,9)])
delta.y <- as.matrix(subset(diabetes, LASER==2)[c(7,9)])
# Create the input for the test:
z <- rbind(x, y)
delta.z <- rbind(delta.x, delta.y)
# Run the test with 50 permutations:
perm_mvlogrank(B = 50, z, delta.z, n1 = nrow(x))
# In most cases, it is preferable to use more than 50
# permutations for computing p-values. choose_B() can
# be used to determine how many permutations are needed.