survidm (version 1.3.2)

markov.test: This function is used to test the markov assumption in the illness-death model.

Description

The markov assumption may be tested including the sojourn time in the initial state, "times1", and other covariates in the Cox model.

Usage

markov.test(formula, s, nm.method = "LM", data)

Arguments

formula

A formula object, which must have a survIDM.

s

The first time for obtaining a graphical test of markovianity by comparison of the estimates for transition probabilities. If missing, first quartile of the sojourn time in the initial state has been considered for the graphical test.

nm.method

The non-markov method used to compute the transition probabilities. Defaults to "LM".

data

A data.frame including at least four columns named time1, event1, Stime and event, which correspond to disease free survival time, disease free survival indicator, time to death or censoring, and death indicator, respectively.

Value

cox.markov.test

An object of class coxph representing the fit. See coxph.object for details.

TPestimates

Dataframe with estimates of the transition probabilities for Aalen-Johansen estimator (markovian) and for non-markov estimator. Confidence intervals for the transition probability from State 1 to State 2 are also available.

nm.method

The non-markov method used to compute the transition probabilities.

s

The first time for obtaining a graphical test of markovianity by comparison of the estimates for transition probabilities.

call

A call object.

Details

The markov assumption may be tested including the sojourn time in the initial state, "times1", and other covariates in the Cox model. A graphical test for Markovianity is also available.

References

L. Meira-Machado, J. de Una-Alvarez, C. Cadarso-Suarez, and P. Andersen. Multi-state models for the analysis of time to event data. Statistical Methods in Medical Research, 18:195-222, 2009.

J. de Una-Alvarez and L. Meira-Machado. Nonparametric estimation of transition probabilities in the non-markov illness-death model: A comparative study. Biometrics, 71(2):364-375, 2015.

L. Meira-Machado and M. Sestelo. Estimation in the progressive illness-death model: A nonexhaustive review. Biometrical Journal, 2018.

Examples

# NOT RUN {
mk <- markov.test(survIDM(time1,event1,Stime,event)~1, s=365, nm.method = "LM", data=colonIDM)
mk$cox.markov.test
mk$TPestimates
mk$nm.method
plot(mk)

# }