Summary
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 <b>msm</b> 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 <b>msm</b> to model chronic disease progression and screening. Assessment of model fit, and potential future developments of the software, are also discussed.
Outcomes reported
Referenced by PLOS supermarket placement trial as citation 43; likely supports topic area: methods / modelling / statistics. Topics: methods / modelling / statistics Evidence type: Modelling / projection Source report: PLOS supermarket placement trial Ref#: PLOS supermarket placement trial #43 Original: Jackson CH. Multi-state models for panel data: the msm package for R. J Stat Soft. 2011;38(8). https://doi.org/10.18637/jss.v038.i08
Dig deeper with Pulse AI.
Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.