The inference in multi-state models is traditionally performed under
a Markov assumption that claims that past and future of the process
are independent given the present state. In this package, we
consider 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
Markov 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 (see Soutinho G, Meira-Machado L
(2021)
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 [email protected]
Maintainer: Gustavo Soutinho [email protected]
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.