An R package for inference and prediction in an illness-death model
| dc.contributor.author | Machado, Luís Meira | por |
| dc.contributor.author | Sestelo, Marta | por |
| dc.date.accessioned | 2021-01-22T11:28:00Z | |
| dc.date.available | 2021-01-22T11:28:00Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Multi-state models are a useful way of describing a process in which an individual moves through a number of nite 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, prevalence and the sojourn time distributions. An R package was built providing answers to all these topics. | por |
| dc.distribution | international | por |
| dc.identifier.uri | https://hdl.handle.net/1822/69573 | |
| dc.language.iso | eng | por |
| dc.peerreviewed | yes | por |
| dc.rights | openAccess | por |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
| dc.subject | Illness-death model | por |
| dc.subject | Kaplan-Meier | por |
| dc.subject | Landmark approach | por |
| dc.subject | Nonparametric estimation | por |
| dc.subject | Survival analysis | por |
| dc.subject.fos | Ciências Naturais::Matemáticas | por |
| dc.title | An R package for inference and prediction in an illness-death model | por |
| dc.type | conferencePaper | por |
| dspace.entity.type | Publication | en |
| oaire.version | AO | por |
| sdum.conferencePublication | 33rd International Workshop on Statistical Modelling | por |
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