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calibmsm
Calibration Plots for the Transition Probabilities from Multistate Models
View on CRAN: Click
here
Download and install calibmsm package within the R console
Install from CRAN:
install.packages("calibmsm")
Install from Github:
library("remotes")
install_github("cran/calibmsm") Install by package version:
library("remotes")
install_version("calibmsm", "1.1.1") Attach the package and use:
library("calibmsm")
Maintained by
Alexander Pate
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
https://alexpate30.github.io/calibmsm/articles/Overview.html . 10.32614/CRAN.package.calibmsm . https://alexpate30.github.io/calibmsm/ . calibmsm citation info . calibmsm results . calibmsm.pdf . A guide on how to use calibmsm . calibmsm_1.1.1.tar.gz . calibmsm_1.1.1.zip . calibmsm_1.1.1.zip . calibmsm_1.1.1.zip . calibmsm_1.1.1.tgz . calibmsm_1.1.1.tgz . calibmsm_1.1.1.tgz . calibmsm_1.1.1.tgz . calibmsm_1.1.1.tgz . calibmsm_1.1.1.tgz . calibmsm archive . https://CRAN.R-project.org/package=calibmsm .
First Published: 2023-11-30
Latest Update: 2023-11-30
Description:
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)
and Pate et al. (2023) .
How to cite:
Alexander Pate (2023). calibmsm: Calibration Plots for the Transition Probabilities from Multistate Models. R package version 1.1.1, https://cran.r-project.org/web/packages/calibmsm. Accessed 29 Mar. 2025.
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Complete documentation for calibmsm
Functions, R codes and Examples using
the calibmsm R package
Some associated functions: calc_aj . calc_pv_aj . calc_weights . calib_blr . calib_mlr . calib_pv . calibmsm-package . ebmtcal . extract_ids_states . msebmtcal.cmprsk . msebmtcal . plot.calib_blr . plot.calib_mlr . plot.calib_pv . tp.cmprsk.j0 . tps0 . tps100 .
Some associated R codes: calib_blr.R . calib_mlr.R . calib_pv.R . calibmsm-misc.R . calibmsm-package.R . data.R . extract_ids_states.R . plot_calib.R . weights.R . Full calibmsm package functions and examples
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