Nonparametric Models for Longitudinal Data

Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data. This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are balanced between theoretical and practical topics. The statistical applications in real-life examples lead into meaningful interpretations and inferences. Features:• Provides an overview of parametric and semiparametric methods • Shows smoothing methods for unstructured nonparametric models • Covers structured nonparametric models with time-varying coefficients • Discusses nonparametric shared-parameter and mixed-effects models • Presents nonparametric models for conditional distributions and functionals • Illustrates implementations using R software packages • Includes datasets and code in the authors’ website • Contains asymptotic results and theoretical derivations

"This book will be a good reference book in the area of longitudinal data. It provides a self-contained treatment of structured nonparametric models for longitudinal data. Three commonly used method-kernel, polynomial splines, penalization/smoothing splines are all treated with enough depth. The book's coverage of time-varying coefficient models is comprehensive. Shared-parameter and mixed-effects models are very useful in longitudinal data analysis. Section V 'Nonparametric Models for Distribution' summarizes recent development on a new class of models. This section alone will make the book unique among all published books in longitudinal data analysis. Another unique feature of the book is the use of four actual longitudinal studies presented in Section 1.2. The book used data from these four studies when introducing every model/method. This approach motivates each method very well and also shows the usefulness of the method...The book will be an excellent addition to the literature." ~Jianhua Huang, Texas A&M University". . . , this book provides a comprehensive review of the structure of longitudinal data, as well as several applicable nonparametric models. It should prove useful for those working with biomedical data, including real world evidence. The focus is on theorems and proofs; references demonstrate the depth of the research. Real life examples are provided in the form of data descriptions, as well as R code and output. These examples will allow the reader to employ the models and gain expertise in the interpretation of the R output."~Journal of Biopharmaceutical Statistics, Darcy Hille, Merck Research Laboratories"The authors are to be commended for such a thorough and well written book, which would certainly be of interest to anyone involved in analysing complex longitudinal data or with an interest in nonparametric approaches."~David Hughes, ISCB Newsletter

Both authors are mathematical statisticians at the National Institutes of Health (NIH) and have published extensively in statistical and biomedical journals. Colin O. Wu earned his Ph.D. in statistics from the University of California, Berkeley (1990), and is also Adjunct Professor at the Georgetown University School of Medicine. He served as Associate Editor for Biometrics and Statistics in Medicine, and reviewer for National Science Foundation, NIH, and the U.S. Department of Veterans Affairs. Xin Tian earned her Ph.D. in statistics from Rutgers, the State University of New Jersey (2003). She has served on various NIH committees and collaborated extensively with clinical researchers.