mstate: An R Package for the Analysis of Competing Risks and Multi-State Models

By Liesbeth C. de Wreede, Marta Fiocco, Hein Putter. 2011

486 citations recorded by the source corpus.

Multi-state models are a very useful tool to answer a wide range of questions in survival analysis that cannot, or only in a more complicated way, be answered by classical models. They are suitable for both biomedical and other applications in which time-to-event variables are analyzed. However, they are still not frequently applied. So far, an important reason for this has been the lack of available software. To overcome this problem, we have developed the mstate package in R for the analysis of multi-state models. The package covers all steps of the analysis of multi-state models, from model building and data preparation to estimation and graphical representation of the results. It can be applied to non- and semi-parametric (Cox) models. The package is also suitable for competing risks models, as they are a special category of multi-state models. This article offers guidelines for the actual use of the software by means of an elaborate multi-state analysis of data describing post-transplant events of patients with blood cancer. The data have been provided by the EBMT (the European Group for Blood and Marrow Transplantation). Special attention will be paid to the modeling of different covariate effects (the same for all transitions or transition-specific) and different baseline hazard assumptions (different for all transitions or equal for some).

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8ae56b970412724a58dc07c1f14f61967b5644f3

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Citations in the corpus

Multi-State Models for Panel Data: The msm Package for R (2011, methodology): “For example, proportional hazards models with non-parametric baseline intensities can be fitted using the mstate package (de Wreede et al. 2011, 2010) Generally, msm allows a dataset to be an arbitrary mixture of observations such that states are panel-observed, continuously-observed, or “exact…”