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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.
InstallationIf 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.