dc.contributor.author | Azarang, L. | |
dc.contributor.author | Oviedo de la Fuente, M. | |
dc.date.accessioned | 2020-03-02T18:55:44Z | |
dc.date.available | 2020-03-02T18:55:44Z | |
dc.date.issued | 2018-12-01 | |
dc.identifier.issn | 2073-4859 | |
dc.identifier.uri | http://hdl.handle.net/20.500.11824/1089 | |
dc.description.abstract | The progressive illness-death model is frequently used in medical applications. For example, the model may be used to describe the disease process in cancer studies. We have developed a new R package called idmTPreg to estimate regression coefficients in datasets that can be described by the progressive illness-death model. The motivation for the development of the package is a recent contribution that enables the estimation of possibly time-varying covariate effects on the transition probabilities for a progressive illness-death data. The main feature of the package is that it befits both non-Markov and Markov progressive illness-death data. The package implements the introduced estimators obtained using a direct binomial regression approach. Also, variance estimates and confidence bands are implemented in the package. This article presents guidelines for the use of the package. | en_US |
dc.description.sponsorship | BERC 2014-2017
SEV-2013-0323
MTM2016-76969-P
FP7/2011: Marie Curie Initial Training Network MEDIASRES | en_US |
dc.format | application/pdf | en_US |
dc.language.iso | eng | en_US |
dc.rights | Reconocimiento-NoComercial-CompartirIgual 3.0 España | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | en_US |
dc.title | idmTPreg: Regression Model for Progressive Illness Death Data | en_US |
dc.type | info:eu-repo/semantics/article | en_US |
dc.relation.publisherversion | https://journal.r-project.org/archive/2018/RJ-2018-081/RJ-2018-081.pdf | en_US |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en_US |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | en_US |
dc.journal.title | The R Journal | en_US |