Joint Modeling of Longitudinal and Time-To-Event Data - Info and Reading Options

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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. Examples of topics: Longitudinal data analysis with non-ignorable monotone and intermittent missing data. Event time models with intermittently measured time-dependent covariates. Longitudinal studies with informative observation times. Joint models for competing risks, multivariate longitudinal, and multivariate survival outcomes, Dynamic prediction, Modeling longitudinal data shortly before death, 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. Book jacket.

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