`markovMSM`

is an R package which considers tests of the
Markov assumption that are applicable to general multi-state models.
Three approaches using existing methodology are considered: a simple
method based on including covariates depending on the history in Cox
models for the transition intensities; methods based on measuring the
discrepancy of the non-Markov estimators of the transition probabilities
to the Markovian Aalen-Johansen estimators; and, finally, methods that
were developed by considering summaries from families of log-rank
statistics where patients are grouped by the state occupied of the
process at a particular time point.

`Installation`

If you want to use the release version of
the markovMSM package, you can install the package from CRAN as follows:
install.packages(pkgs=“markovMSM”);

`Authors`

Gustavo Soutinho and Luís Meira-Machado
lmachado@math.uminho.pt Maintainer: Gustavo Soutinho
gustavosoutinho@sapo.pt

`Funding`

This research was financed by Portuguese Funds
through FCT - “Fundação para a Ciência e a Tecnologia”, within Projects
projects UIDB/00013/2020, UIDP/00013/2020 and the research grant
PD/BD/142887/2018.

`References`

Aalen O, Johansen S (1978). “An Empirical
transition matrix for non homogeneous Markov and chains based on
censored observations.” Scandinavian Journal of Statistics, 5,
141–150.

Andersen P, Esbjerg S, Sorensen T (2000). “Multistate models for bleeding episodes and mortality in liver cirrhosis.” Statistics in Medicine, (19), 587–599.

Andersen P, Keiding N (2002). “Multi-state models for event history analysis.” Statistical Methods in Medical Research, (11), 91–115.

Andersen PK, Borgan Ø, Gill RD, Keiding N (1993). Statistical Models Based on Counting Processes. Springer-Verlag, New York.

Borgan O (2005). Encyclopedia of biostatistics: Aalen-Johansen estimator. John Wiley & Sons.

Chiou S, Qian J, Mormino E, Betensky R (2018). “Permutation tests for general dependent truncation.” Computational Statistics & Data Analysis, 318, 308–324. doi:10.1016/j. csda.2018.07.012.

Datta S, Satten G (2001). “Validity of the Aalen-Johansen estimators of stage occupation probabilities and Nelson Aalen integrated transition hazards for non-Markov models.” Statistics & Probability Letters, 55, 403–411.

de Uña-Álvarez J, Meira-Machado L (2015). “Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study.” Biometrics, 71(2), 364–375. ISSN 0006-341X.

Hougaard P (2000). Analysis of Multivariate Survival Data. Statistics for Biology and Health. Springer-Verlag, New York.

Kay R (1986). “A Markov model for analyzing cancer markers and disease states in survival studies.” Biometrics, (42), 457–481. Meira-Machado L, de Uña-Álvarez J, Cadarso-Suárez C (2006). “Nonparametric Estimation of Transition Probabilities in a Non-Markov Illness-Death Model.” Lifetime Data Analysis, 12, 325–344.