Articoli correlati a Innovative Statistical Methods for Public Health Data

Innovative Statistical Methods for Public Health Data - Brossura

Libro 3 di 19: ICSA Book Series in Statistics
 
9783319366418: Innovative Statistical Methods for Public Health Data
Brossura
ISBN 10: 3319366416 ISBN 13: 9783319366418
Casa editrice: Springer, 2016

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull'autore

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.

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer
Data di pubblicazione
2016
Lingua
Inglese
ISBN 10 
3319366416
ISBN 13 
9783319366418
Rilegatura
Copertina flessibile
Numero di pagine
368
Redattore
Chen Ding-Geng (Din), Wilson Jeffrey
Contatto del produttore
Springer Nature Customer Service Center GmbH
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