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Tuesday, July 28, 2020 | History

1 edition of Joint models for longitudinal and time-to-event data found in the catalog.

Joint models for longitudinal and time-to-event data

Dimitris Rizopoulos

Joint models for longitudinal and time-to-event data

with applications in R

by Dimitris Rizopoulos

  • 103 Want to read
  • 33 Currently reading

Published by CRC Press in Boca Raton .
Written in English

    Subjects:
  • MATHEMATICS / Probability & Statistics / General,
  • MEDICAL / Epidemiology,
  • Numerical analysis,
  • Data processing,
  • R (Computer program language)

  • Edition Notes

    Includes bibliographical references and index.

    StatementDimitris Rizopoulos
    SeriesChapman & Hall/CRC biostatistics series -- 6
    Classifications
    LC ClassificationsQA279 .R59 2012
    The Physical Object
    Paginationp. cm.
    ID Numbers
    Open LibraryOL25330790M
    ISBN 109781439872864
    LC Control Number2012014570

    Buy Joint Modeling of Longitudinal and Time-to-Event Data (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) 1 by Elashoff, Robert, li, Gang, Li, Ning (ISBN: ) from Amazon's Book Store. Everyday low prices and free delivery on eligible : Robert Elashoff, Gang li, Ning Li.   Abstract. In longitudinal studies measurements are often collected on different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death, development of a disease or dropout from the study).Cited by: 4.

    Get this from a library! Joint models for longitudinal and time-to-event data: with applications in R. [Dimitris Rizopoulos] -- "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 . Using these augmented longitudinal responses, residuals are then calculated for the complete data, and a multiple imputation approach is used to properly account for the uncertainty in the imputed values due to missingness (Gelman et al., ).Author: Dimitris Rizopoulos.

    Joint models for longitudinal and survival data are particularly relevant to many cancer clinical trials and observational studies in which longitudinal biomarkers (eg, circulating tumor cells, immune response to a vaccine, and quality-of-life measurements) may be highly associated with time to event, such as relapse-free survival or overall survival. In this article, we give an introductory Cited by:   Joint models for longitudinal and survival data are particularly relevant to many cancer clinical trials and observational studies in which longitudinal biomarkers (eg, circulating tumor cells, immune response to a vaccine, and quality-of-life measurements) may be highly associated with time to event, such as relapse-free survival or overall by:


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Joint models for longitudinal and time-to-event data by Dimitris Rizopoulos Download PDF EPUB FB2

"This book provides an extensive survey of research performed on the subject of joint models in longitudinal and time-to-event data. The authors’ expertise in this area shines through their careful attention to detail in presenting the wide variety of settings in which these models can be applied.

Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data.

The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data.

The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are Cited by: Joint modeling of longitudinal and time-to-event data has emerged as a novel approach to handle these issues.

Joint Modeling of Longitudinal and Time-to-Event Data provides a systematic introduction and review of state-of-the-art statistical methodology in this active research field. The methods are illustrated by real data examples from a wide Cited by: 9.

Joint modeling of longitudinal and time-to-event data has emerged as a novel approach to handle these issues. Joint Modeling of Longitudinal and Time-to-Event Data provides a systematic introduction and review of state-of-the-art statistical methodology in this active research field.

References Joint modeling sources Rizopoulos, D. Joint Models for Longitudinal and Time-to-Event Data, with Applications in Raton: Chapman & Hall/ Size: KB. Joint models for longitudinal and time-to-event data with applications in R.

CRC Press, The R package JM: Download JM package: Li, Z., Tosteson, T.D. and Bakitas, M.A. [] Joint modeling quality of life and survival using a terminal decline model in palliative care studies. Statistics in Medicine () 32, SAS.

The last 20 years have seen an increasing interest in the class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First, when focus is on a survival outcome and we wish to account for the effect of an endogenous.

AbstractMethodological development and clinical application of joint models of longitudinal and time-to-event outcomes have grown substantially over the past two decades. However, much of this research has concentrated on a single longitudinal outcome and a single event time outcome.

In clinical and public health research, patients who are followed up over time may often experience multiple Cited by: 2. Joint Models for Longitudinal and Survival Data Dimitris Rizopoulos Department of Biostatistics, Erasmus University Medical Center time-to-event(s) of particular interest (e.g., death, Joint Models for Longitudinal and Time-to-Event Data, with Applications in R.

Boca Raton: Chapman & Hall/ Size: KB. Get this from a library. Joint modeling of longitudinal and time-to-event data. [Robert M Elashoff; Gang Li; Ning Li, (Mathematician)] -- In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an.

longitudinal response process and a time-to-event. Considerable recent interest has focused on so-called joint models, where models for the event time distribution and longitudinal data are taken to depend on a common set of latent random efiects. In the literature, precise statement of the underlying assumptions typically made for these.

Dr. Rizopoulos wrote his dissertation, as well as a number of methodological articles on various aspects of joint models for longitudinal and time-to-event data. He currently serves as an Associate Editor for Biometrics and Biostatistics, and has been a guest editor for a special issue in joint modeling techniques in Statistical Methods in /5(3).

Joint models for longitudinal and time-to-event data are commonly used to simultaneously analyse correlated data in single study cases. Synthesis of evidence from multiple studies using meta-analysis is a natural next step but its feasibility depends heavily on the standard of reporting of joint models in the medical by: 9.

Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach a data structure very common in life sciences. 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 the focus is on the survival outcome and we wish to account for the effect of an endogenous time-dependent covariate measured with error, and second, when the focus.

Joint modeling of longitudinal and time-to-event data has emerged as a novel approach to handle these issues. Joint Modeling of Longitudinal and Time-to-Event Data provides a systematic introduction and review of state-of-the-art statistical methodology in this active research field.

The methods are illustrated by real data examples from a wide Cited by: 9. Joint models for longitudinal and time-to-event data constitute an attractive modeling framework that has received a lot of interest in the recent years.

This paper presents the capabilities of the R package JMbayes for fitting these models under a Bayesian approach using Markov chain Monte Carlo algorithms.

JMbayes can fit a wide range of Cited by:   Joint modelling of longitudinal and time-to-event outcomes has received considerable attention over recent years. Commensurate with this has been a rise in statistical software options for fitting these models. However, these tools have generally been limited to a single longitudinal outcome.

Here, we describe the classical joint model to the case of multiple longitudinal outcomes, propose a Cited by: 5. Joint Modeling of Longitudinal and Time-to-Event Data Book Examples.

Scleroderma Lung Study (SLS) Download Data for the Scleroderma Lung Study The Scleroderma Lung Study, a center double-blind, randomized, placebocontrolled trial sponsored by the National Institutes of Health, was designed to evaluate the effectiveness and safety of oral cyclophosphamide for one year in patients with active.

Joint Modeling of Longitudinal and Time-to-Event Data Robert M. Elashoff, Gang Li, and Ning Li CRC Press pages $ Hardcover Monographs on Statistics and Applied Probability; QA The book introduces and reviews statistical methodology developed recently for the joint modeling of longitudinal and survival data.Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that .Joint Models for Longitudinal and Time‐to‐Event Data with Applications in Rizopoulos, R.

Dimitris (). Boca Raton: Chapman & Hall/CRC Texts in Statistical Science Series. pages, ISBN: ‐Author: Maral Saadati.