Covers statistical methods and their applications to public health research in a multi-disciplinary approach by experts in the field
Compiles the data & related software in innovative statistical methods so readers can use the software for their own data analysis
Shares important implications for model development and data analysis
Can serve as reference for public health and biomedical research and as a text for use in courses on causal inference at the graduate level
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.
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.
Part 1: Modelling Clustered Data.- Methods for Analyzing Secondary Outcomes in Public Health Case Control Studies.- Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems.- On the Inference of Partially Correlated Data with Applications to Public Health Issues.- Modeling Time-Dependent Covariates in Longitudinal Data Analyses.- Solving Probabilistic Discrete Event Systems with Moore-Penrose Generalized Inverse Matrix Method to Extract Longitudinal Characteristics from Cross-Sectional Survey Data.- Part II: Modelling Incomplete or Missing Data.- On the Effects of Structural Zeros in Regression Models.- Modeling Based on Progressively Type-I Interval Censored Sample.- Techniques for Analyzing Incomplete Data in Public Health Research.- A Continuous Latent Factor Model for Non-ignorable Missing Data.- Part III: Healthcare Research Models.- Health Surveillance.- Standardization and Decomposition Analysis: A Useful Analytical Method for Outcome Difference, Inequality and Disparity Studies.- Cusp Catastrophe Modeling in Medical and Health Research.- On Ranked Set Sampling Variation and its Applications to Public Health Research.- Weighted Multiple Testing Correction for Correlated Endpoints in Survival Data.- Meta-analytic Methods for Public Health Research.
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.
"The book is a compilation of new developments instatistical methods and applications relevant in public health research. ... Theprimary audience is statisticians and researchers in biomedical and publichealth research. ... Each chapter ends with a set of references for furtherreading. ... This is an excellent book, with chapters addressing innovativestatistical methods for specific statistical situations, targeted atresearchers in the biomedical or public health fields.” (Kamesh Sivagnanam, Doody´sBook Reviews, January, 2016)
Part 1: Modelling Clustered Data.- Methods for Analyzing Secondary Outcomes in Public Health Case Control Studies.- Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems.- On the Inference of Partially Correlated Data with Applications to Public Health Issues.- Modeling Time-Dependent Covariates in Longitudinal Data Analyses.- Solving Probabilistic Discrete Event Systems with Moore-Penrose Generalized Inverse Matrix Method to Extract Longitudinal Characteristics from Cross-Sectional Survey Data.- Part II: Modelling Incomplete or Missing Data.- On the Effects of Structural Zeros in Regression Models.- Modeling Based on Progressively Type-I Interval Censored Sample.- Techniques for Analyzing Incomplete Data in Public Health Research.- A Continuous Latent Factor Model for Non-ignorable Missing Data.- Part III: Healthcare Research Models.- Health Surveillance.- Standardization and Decomposition Analysis: A UsefulAnalytical Method for Outcome Difference, Inequality and Disparity Studies.- Cusp Catastrophe Modeling in Medical and Health Research.- On Ranked Set Sampling Variation and its Applications to Public Health Research.- Weighted Multiple Testing Correction for Correlated Endpoints in Survival Data.- Meta-analytic Methods for Public Health Research.
"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)
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 JMIGand 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.
Über den Autor
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 JMIGand 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.
Inhaltsverzeichnis
Part 1: Modelling Clustered Data.- Methods for Analyzing Secondary Outcomes in Public Health Case Control Studies.- Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems.- On the Inference of Partially Correlated Data with Applications to Public Health Issues.- Modeling Time-Dependent Covariates in Longitudinal Data Analyses.- Solving Probabilistic Discrete Event Systems with Moore-Penrose Generalized Inverse Matrix Method to Extract Longitudinal Characteristics from Cross-Sectional Survey Data.- Part II: Modelling Incomplete or Missing Data.- On the Effects of Structural Zeros in Regression Models.- Modeling Based on Progressively Type-I Interval Censored Sample.- Techniques for Analyzing Incomplete Data in Public Health Research.- A Continuous Latent Factor Model for Non-ignorable Missing Data.- Part III: Healthcare Research Models.- Health Surveillance.- Standardization and Decomposition Analysis: A Useful Analytical Method for Outcome Difference, Inequality and Disparity Studies.- Cusp Catastrophe Modeling in Medical and Health Research.- On Ranked Set Sampling Variation and its Applications to Public Health Research.- Weighted Multiple Testing Correction for Correlated Endpoints in Survival Data.- Meta-analytic Methods for Public Health Research.
Klappentext
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.
Covers statistical methods and their applications to public health research in a multi-disciplinary approach by experts in the field
Compiles the data & related software in innovative statistical methods so readers can use the software for their own data analysis
Shares important implications for model development and data analysis
Can serve as reference for public health and biomedical research and as a text for use in courses on causal inference at the graduate level
