LR.test {markovMSM} | R Documentation |
Log-rank based test for the validity of the Markov assumption.
Description
Function LR.test performs the log-rank test described in Titman & Putter (2020).
Usage
LR.test(
data,
times = times,
from,
to,
replicas = 1000,
formula = NULL,
fn = list(function(x) mean(abs(x), na.rm = TRUE)),
fn2 = list(function(x) mean(x, na.rm = TRUE)),
min_time = 0,
other_weights = NULL,
dist = c("poisson", "normal")
)
Arguments
data |
Multi-state data in |
times |
Grid of time points at which to compute the statistic. |
from |
The starting state of the transition to check the Markov condition. |
to |
The last state of the considered transition to check the Markov condition. |
replicas |
Number of wild bootstrap replications to perform. |
formula |
Right-hand side of the formula. If NULL will fit with no covariates (formula="1" will also work), offset terms can also be specified. |
fn |
A list of summary functions to be applied to the individual zbar traces (or a list of lists) |
fn2 |
A list of summary functions to be applied to the overall chi-squared trace |
min_time |
The minimum time for calculating optimal weights |
other_weights |
Other (than optimal) weights can be specified here |
dist |
Distribution of wild bootstrap random weights, either "poisson" for centred Poisson (default), or "normal" for standard normal |
Value
LR.test returns an object of class "markovMSM", which is a list with the following items:
localTestLR |
p-value of AUC local tests for each times and transitions. |
globalTestLR |
p-value of AUC global tests for each transition |
times |
Grid of time points at which to compute the statistic. |
replicas |
Number of wild bootstrap replications to perform. |
call |
Expression of the LR.test used. |
Author(s)
Gustavo Soutinho and Luis Meira-Machado.
References
Titman AC, Putter H (2020). General tests of the Markov property in multi-state models. Biostatistics.
Examples
set.seed(1234)
library(markovMSM)
data("colonMSM")
positions<-list(c(2, 3), c(3), c())
namesStates = c("Alive", "Rec", "Death")
tmat <-transMatMSM(positions, namesStates)
timesNames = c(NA, "time1","Stime")
status=c(NA, "event1","event")
trans = tmat
db_long<- prepMSM(data=colonMSM, trans, timesNames, status)
res<-LR.test(data=db_long, times=180, from = 2, to = 3, replicas = 1000)
res$globalTestLR
times<-c(73.5, 117, 223, 392, 681)
res2<-LR.test(data=prothr, times=times, from = 2, to = 3, replicas = 1000)
res2$localTestLR
res2$globalTestLR
res3<-LR.test(data=prothr, times=times, from = 2, to = 1, replicas = 1000)
res3$localTestLR
res3$globalTestLR