rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Joint models for longitudinal and time-to-event data
    sub title: with applications in R
  name:
    name part: Rizopoulos, Dimitris.
    role:
      role term: creator
  name:
    name part: ebrary, Inc
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (273 pages)
  abstract: "Preface Joint models for longitudinal and time-to-event data have become a valuable tool in the analysis of follow-up data. These models are applicable mainly in two settings: First, when focus is in the survival outcome and we wish to account for the effect of an endogenous time-dependent covariate measured with error, and second, when focus is in the longitudinal outcome and we wish to correct for nonrandom dropout. Due to their capability to provide valid inferences in settings where simpler statistical tools fail to do so, and their wide range of applications, the last 25 years have seen many advances in the joint modeling field. Even though interest and developments in joint models have been widespread, information about them has been equally scattered in articles, presenting recent advances in the field, and in book chapters in a few texts dedicated either to longitudinal or survival data analysis. However, no single monograph or text dedicated to this type of models seems to be available. The purpose in writing this book, therefore, is to provide an overview of the theory and application of joint models for longitudinal and survival data. In the literature two main frameworks have been proposed, namely the random effects joint model that uses latent variables to capture the associations between the two outcomes (Tsiatis and Davidian, 2004), and the marginal structural joint models based on G estimators (Robins et al., 1999, 2000). In this book we focus in the former. Both subfields of joint modeling, i.e., handling of endogenous time-varying covariates and nonrandom dropout, are equally covered and presented in real datasets"--
  table of contents: 1. Introduction -- 2. Longitudinal data analysis -- 3. Analysis of event time data -- 4. Joint models for longitudinal and time-to-event data -- 5. Extensions of the standard joint model -- 6. Joint model diagnostics -- 7. Prediction and accuracy in joint models.
  note: Dimitris Rizopoulos.
  note: Electronic resource.
  note: Includes bibliographical references and index.
  subject:
    topic: Numerical Analysis, Computer-Assisted
  subject:
    topic: Numerical analysis
    topic: Data processing
  subject:
    topic: R (Computer program language)
  subject:
    topic: MATHEMATICS / Probability & Statistics / General
  subject:
    topic: MEDICAL / Epidemiology
  classification: QA279 .R59 2012eb
  classification: 518
  related item:
    title info:
      title: Joint models for longitudinal and time-to-event data
    name:
      name part: Rizopoulos, Dimitris.
    origin info:
      publisher: Boca Raton : CRC Press, 2012
    identifier: (DLC) 2012014570
    identifier: (OCoLC)698322708
  related item:
    title info:
      title: Chapman & Hall/CRC biostatistics series ; 6
  identifier: 9781439872864 (hardback)
  identifier: 1439872864 (hardback)
  identifier: 9781439872871
  identifier: 1439872872
  identifier: 2012014570
  identifier: https://ebookcentral.proquest.com/lib/tamucs/detail.action
  record info:
    record content source: GZM
    record creation date: 120729
    record change date: 20250816213242.2
    record identifier: in00002777050
    language of cataloging:
      language term: eng