markov.test: This function is used to test the markov assumption in the...
In survidm: Inference and Prediction in an Illness-Death Model

Description Usage Arguments Details Value Author(s) References Examples

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

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

`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.

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.

`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.

Luis Meira-Machado, Marta Sestelo and Gustavo Soutinho.

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.

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)