Springer Innovative Statistical Methods for Public Health Data (ICSA Book Series in Statistics)

Product Description The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference and it can be used in graduate level classes. Review “The book is a compilation of new developments in statistical methods and applications relevant in public health research. … The primary audience is statisticians and researchers in biomedical and public health research. … Each chapter ends with a set of references for further reading. … This is an excellent book, with chapters addressing innovative statistical methods for specific statistical situations, targeted at researchers in the biomedical or public health fields.” (Kamesh Sivagnanam, Doody’s Book Reviews, January, 2016) From the Back Cover The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference, and it can be used in graduate level classes. About the Author Ding-Geng (Din) Chen (PhD in Statistics from University of Guelph) is a professor in biostatistics at the University of Rochester. Previously, he was the Karl E. Peace endowed eminent scholar chair in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University. He is also a senior biostatistics consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trials and bioinformatics. He has more than 100-refereed professional publications and co-authored five books in biostatistics. Professor Chen was Section Chair (2011-2014) of Applied Public Health Statistics, American Public Health Association. Professor Jeffrey Wilson was Section Chair (2010-2013) of Applied Public Health Statistics, American Public Health Association. He was also a former Director of Biostatistics Core in the NIH Center Alzheimer. He is also the former Director of the School of Health Management and Policy. He is an Associate Editor for The JMIG and Chair of the Editorial Board of AJPH. His research experience includes grants from the NSF, USDA and NIH. He has published several articles in leading journals in Statistics and Healthcare. He teaches statistics at the graduate level in topics including GLM and GLIMMIX.