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Joint Models for Longitudinal and Time-to-Event Data: With Applications in R (Chapman & Hall/CRC Biostatistics Series) - Softcover
Inhaltsangabe
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 event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. 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 provided to facilitate understanding of the key features of these models.
All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author.
All the R code used in the book is available at:
http://jmr.r-forge.r-project.org/
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Über die Autorin bzw. den Autor
Dimitris Rizopoulos is an Assistant Professor at the Department of Biostatistics of the Erasmus University Medical Center in the Netherlands. Dr. Rizopoulos received his M.Sc. in Statistics in 2003 from the Athens University of Economics and Business, and a Ph.D. in Biostatistics in 2008 from the Katholieke Universiteit Leuven.
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 Medical Research.
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Joint Models for Longitudinal and Time-to-Event Data: With Applications in R
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Joint Models for Longitudinal and Time-to-Event Data (Chapman & Hall/CRC Biostatistics Series)
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Joint Models for Longitudinal and Time-to-Event Data (Paperback)
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Paperback. Zustand: new. Paperback. 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 event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. 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 provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author.All the R code used in the book is available at:http://jmr.r-forge.r-project.org/ Longitudinal studies often investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest. An example is prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. This book provides a full treatment of joint models for longitudinal a Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781032477565
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Joint Models for Longitudinal and Time-to-Event Data: With Applications in R
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Joint Models for Longitudinal and Time-to-Event Data
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. 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 provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author.All the R code used in the book is available at:jmr.r-forge.r-project.org/ 278 pp. Englisch. Bestandsnummer des Verkäufers 9781032477565
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