Search results for ""Author Hulin Wu""

With contributions from an international team of leading researchers, the book pulls together updated research results in the area of HIV/AIDS modeling to provide readers with the latest information in the field. Topics covered include: AIDS epidemic models; vaccine models; models for HIV/cell dynamics and interactions; cellular kinetics; viral dynamics with antiviral treatments; modeling of drug resistance and quasispecies.Extensive deterministic models, statistical models, stochastic models and state space models on treating AIDS patients with anti-retroviral drugs are provided, as well as an in-depth discussion of these models. The book also contains updated reviews on mathematical models for assessing effects of AIDS vaccines, statistical methods for analyzing clinical trial data on AIDS vaccines, and overviews of models and statistical methods for assessing drug resistance of HIV to anti-retroviral drugs. Some important statistical methods specific to the intervention and prevention of HIV epidemic are also discussed.This will be a useful reference source for graduate students and researchers in biomathematics and biostatistics, as well as for HIV/AIDS epidemiologists and clinical investigators learning quantitative methods to study AIDS epidemics and HIV infection.

Read more less

Incorporates mixed-effects modeling techniques for more powerful and efficient methods This book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented. With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques. The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis. Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices. With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems.

Read more less