## calibmsm: Calibration Plots for the Transition Probabilities from
Multistate Models

Assess the calibration of an existing (i.e. previously developed) multistate
model through calibration plots.
Calibration is assessed using one of three methods. 1) Calibration methods for
binary logistic regression models applied at a fixed time point in conjunction
with inverse probability of censoring weights. 2) Calibration methods for
multinomial logistic regression models applied at a fixed time point in conjunction
with inverse probability of censoring weights. 3) Pseudo-values estimated using
the Aalen-Johansen estimator of observed risk. All methods are applied in conjunction
with landmarking when required. These calibration plots evaluate the calibration
(in a validation cohort of interest) of the transition probabilities estimated from an
existing multistate model. While package development has focused on multistate
models, calibration plots can be produced for any model which utilises information
post baseline to update predictions (e.g. dynamic models); competing risks models;
or standard single outcome survival models, where predictions can be made at
any landmark time. The underpinning methodology is currently undergoing peer review; see Pate et al. (2023) <arXiv:2308.13394>
and Pate et al. (2023) <https://alexpate30.github.io/calibmsm/articles/Overview.html>.

Version: |
1.0.0 |

Depends: |
R (≥ 2.10) |

Imports: |
boot, dplyr, ggplot2, ggpubr, Hmisc, magrittr, mstate, rms, stats, survival, tidyr, VGAM |

Suggests: |
covr, knitr, rmarkdown, testthat (≥ 3.0.0) |

Published: |
2023-11-30 |

Author: |
Alexander Pate
[aut, cre, cph],
Glen P Martin
[fnd, rev] |

Maintainer: |
Alexander Pate <alexander.pate at manchester.ac.uk> |

License: |
MIT + file LICENSE |

URL: |
https://alexpate30.github.io/calibmsm/ |

NeedsCompilation: |
no |

Materials: |
README NEWS |

CRAN checks: |
calibmsm results |

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