Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/32346

TitleTPmsm : estimation of the transition probabilities in 3-state models
Author(s)Araújo, Artur
Machado, Luís Meira
Roca-Pardiñas, Javier
KeywordsSurvival
Multi-state model
Kaplan-Meier
Illness-death model
Transition probabilities
Issue date2014
PublisherFoundation for Open Access Statistics
JournalJournal of Statistical Software
Abstract(s)One major goal in clinical applications of multi-state models is the estimation of transitionprobabilities. The usual nonparametric estimator of the transition matrix for nonhomogeneous Markov processes is the Aalen-Johansen estimator (Aalen and Johansen 1978). However, two problems may arise from using this estimator: first, its standard error may be large in heavy censored scenarios; second, the estimator may be inconsistent if the process is non-Markovian. The development of the R package TPmsm has been motivated by several recent contributions that account for these estimation problems. Estimation and statistical inference for transition probabilities can be performed using TPmsm. The TPmsm package provides seven di fferent approaches to three-state illness-death modeling. In two of these approaches the transition probabilities are estimated conditionally on current or past covariate measures. Two real data examples are included for illustration of software usage.
TypeArticle
URIhttp://hdl.handle.net/1822/32346
ISSN1548-7660
Publisher versionhttp://www.jstatsoft.org/
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

Files in This Item:
File Description SizeFormat 
v62i04.pdf
  Restricted access
Full paper545,07 kBAdobe PDFView/Open    Request a copy!

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID