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.

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.

A novel strain of influenza A (H1N1) virus emerged and an outbreak of influenza A (H1N1) occurred worldwide in 2009 and 2010, posing a severe threat to the health of human beings. The two Editors work together in Beijing You An Hospital, to combat this type of influenza. This book, Radiology of Influenza A (H1N1), is intended to provide first-hand clinical data and its scientific analysis, thus supporting sound clinical practice for influenza A (H1N1) prevention and treatment as well as related scientific research. The authors have collected over 100 critical influenza A (H1N1) virus-infected cases. For cases of influenza A (H1N1) that resulted in death, autopsies were performed for 8 cases after receiving their families' informed consent. Over 200 reports on pathological and histological analysis as well as immunohistochemical and electroscopic analysis were brought together. This book was then compiled by comparing the published data with the present first-hand data. This book includes a wealth of content, including DR, CT and MRI images, pathological and histological figures, and immunohistochemical and electroscopic figures. Related research, clinical and educational needs can be readily satisfied by its detailed data, texts and figures, as well as typical case reports. Li Hongjun, is professor and director of the diagnostic radiology department in Beijing You An Hospital, affiliated with Capital Medical University, Beijing, China. Li Ning, is professor and president of Beijing You An Hospital, affiliated with Capital Medical University, Beijing, China.

This book constitutes the refereed proceedings of the 11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020, held in Shenzhen, China, in December 2020.The 37 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers deal with research results and development activities in all aspects of parallel architectures, algorithms and programming techniques.