README.md

In markovMSM: Methods for Checking the Markov Condition in Multi-State Survival Data

markovMSM: An R package for checking the Markov condition in multi-state survival data

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 [e-mail] Maintainer: Gustavo Soutinho [e-mail]

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.

markovMSM documentation built on Feb. 16, 2023, 8:34 p.m.