Joint Modeling of Longitudinal and Time-to-Event Data

di Robert Elashoff (Autore)

Gang li (Autore)

Ning Li (Autore)

Taylor & Francis Inc, 2016

Longitudinal studies often incur several problems that challenge standard statistical methods for data analysis. These problems include non-ignorable missing data in longitudinal measurements of one or more response variables, informative observation times of longitudinal data, and survival analysis with intermittently measured time-dependent covariates that are subject to measurement error and/or substantial biological variation. 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 range of clinical research topics. A collection of data sets and software for practical implementation of the joint modeling methodologies are available through the book website. This book serves as a reference book for scientific investigators who need to analyze longitudinal and/or survival data, as well as researchers developing methodology in this field. It may also be used as a textbook for a graduate level course in biostatistics or statistics.

• Autore: Robert Elashoff, Gang li, Ning Li
• Editore: Taylor & Francis Inc
• Collana: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
• Anno: 2016
• Rilegatura: Hardback
• Pagine: 262 p.
• Dimensioni: 234 x 156 mm
• Peso: 498 gr.
• EAN: 9781439807828

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