Multi-State Models for Panel Data: The msm Package for R

By C. Jackson. 2011

1,292 citations recorded by the source corpus.

Panel data are observations of a continuous-time process at arbitrary times, for example, visits to a hospital to diagnose disease status. Multi-state models for such data are generally based on the Markov assumption. This article reviews the range of Markov models and their extensions which can be fitted to panel-observed data, and their implementation in the msm package for R. Transition intensities may vary between individuals, or with piecewise-constant time-dependent covariates, giving an inhomogeneous Markov model. Hidden Markov models can be used for multi-state processes which are misclassified or observed only through a noisy marker. The package is intended to be straightforward to use, flexible and comprehensively documented. Worked examples are given of the use of msm to model chronic disease progression and screening. Assessment of model fit, and potential future developments of the software, are also discussed.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9913436418e5b7448e6bc62157be9c28d8568f8b

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In Management Science and Operations Research

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Citations in the corpus

moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models (2016, methodology): “Several general packages exist for ﬁtting HMMs to data, including depmixS4 (Visser & Speekenbrink 2010), Hidden-Markov (Harte 2015) and msm (Jackson 2011).”
The irace package: Iterated racing for automatic algorithm configuration (2016, methodology): “For sampling from a truncated normal distribution, we use code from the msm package [46] . avoid the bias against the extremes introduced by rounding after sampling from a truncated distribution.”
mstate: An R Package for the Analysis of Competing Risks and Multi-State Models (2011, methodology): “This means that for parametric models other software is needed, such as the R package msm developed by Christo-pher Jackson (see Jackson 2010, 2011).”
phytools: an R package for phylogenetic comparative biology (and other things) (2012, background): “These include the following packages: animation: Xie (2011); calibrate: Graﬀelman (2010); igraph: Csardi & Nepusz (2006); mnormt: Genz & Azzalini (2011); msm: Jackson (2011); numDeriv: Gilbert (2011); quadprog: Turlach & Weingessel (2010).”
flexsurv: A Platform for Parametric Survival Modeling in R. (2016, methodology): “Multi-state models for that type of data (and also exact event times) can be fitted with the msm package for R (Jackson 2011), but are restricted to (piecewise) exponential event time distributions.”