Multi-state models are a useful way of describing a process in which an individual moves through a number of finite states in continuous time. The illness-death model plays a central role in the theory and practice of these models, describing the dynamics of healthy subjects who may move to an intermediate “diseased” state before entering into a terminal absorbing state. In these models, one important goal is the modeling of transition rates which is usually done by studying the relationship between covariates and disease evolution. However, biomedical researchers are also interested in reporting other interpretable results in a simple and summarized manner. These include estimates of predictive probabilities, such as the transition probabilities, occupation probabilities, cumulative incidence functions, and the sojourn time distributions. The development of survidm package has been motivated by recent contribution that provides answers to all these topics. An illustration of the software usage is included using real data.
survidm, p3state.msm, TPmsm, etm, mstate, TP.idm, cmprsk, timereg, msSurv, msm, ggplot2, plotly, survival, KernSmooth
Survival, ClinicalTrials, Distributions, Econometrics, Multivariate, Phylogenetics, SocialSciences, TeachingStatistics, WebTechnologies
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For attribution, please cite this work as
Soutinho, et al., "The R Journal: survidm: An R package for Inference and Prediction in an Illness-Death Model", The R Journal, 2021
BibTeX citation
@article{RJ-2021-070,
author = {Soutinho, Gustavo and Sestelo, Marta and Meira-Machado, Luís},
title = {The R Journal: survidm: An R package for Inference and Prediction in an Illness-Death Model},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-070},
doi = {10.32614/RJ-2021-070},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {70-89}
}