Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

58.848 UPC E-Prints
You are here:
View Item 
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament de Matemàtiques
  • Ponències/Comunicacions de congressos
  • View Item
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament de Matemàtiques
  • Ponències/Comunicacions de congressos
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Longitudinal + reliability = joint modeling

Thumbnail
View/Open
Presentació (944,1Kb)
Share:
 
  View Usage Statistics
Cita com:
hdl:2117/22505

Show full item record
Serrat Piè, CarlesMés informacióMés informacióMés informació
Document typeConference lecture
Defense date2013
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
The aim of this presentation is to introduce joint modelling techniques for the simultaneous analysis of longitudinal time-varying data and time-to-event data. This is an increasing area of interest for the analysis of complex systems. Among others, three main advantages of this approach are: a) it corrects the bias derived from a traditional separate analysis, b) the modelization allows to incorporate and model the between and within correlation among observations and, c) true longitudinal profiles for endogenous covariates can be included in the relative hazard survival sub-model. The relevant benefit of these models is being able to estimate the effect of each subject-specific longitudinal profile in the hazard function for the event of interest, in an adaptive manner. In particular, subject-specific dynamic predictions, like conditional survival functions given the available longitudinal information, can be derived. In order to implement joint models, existing open source libraries in R will be introduced and some illustrations will be given.
CitationSerrat, C. Longitudinal + reliability = joint modeling. A: International Workshop on Simulation-Optimization for Logistics & Production. "2013 CYTED-HAROSA International Workshop on Simulation-Optimization for Logistics & Production November 21-22, 2013 - UOC-Tibidabo, Barcelona : Sessions, Titles & Abstracts". Barcelona: 2013, p. 1-33. 
URIhttp://hdl.handle.net/2117/22505
Publisher versionhttp://dpcs.uoc.edu/joomla/index.php/2013-cyted-harosa/2013-cyted-titles
Collections
  • Departament de Matemàtiques - Ponències/Comunicacions de congressos [1.015]
  • GREMA - Grup de Recerca en Estadística Matemàtica i les seves Aplicacions - Ponències/Comunicacions de congressos [26]
Share:
 
  View Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
CytedHAROSA13-CSerrat.pdfPresentació944,1KbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Contact Us
  • Send Feedback
  • Inici de la pàgina