Innovative Statistical Methods for Public Health Data

1. Front Matter

   Pages i-xiv

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2. Modelling Clustered Data

   1. Front Matter

      Pages 1-1

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   2. Methods for Analyzing Secondary Outcomes in Public Health Case–Control Studies

      • Elizabeth D. Schifano, Haim Bar, Ofer Harel

      Pages 3-15

   3. Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems

      • Lynn A. Agre, N. Andrew Peterson, James Brady

      Pages 17-30

   4. On the Inference of Partially Correlated Data with Applications to Public Health Issues

      • Hani M. Samawi, Robert Vogel

      Pages 31-55

   5. Modeling Time-Dependent Covariates in Longitudinal Data Analyses

      • Trent L. Lalonde

      Pages 57-79

   6. Solving Probabilistic Discrete Event Systems with Moore–Penrose Generalized Inverse Matrix Method to Extract Longitudinal Characteristics from Cross-Sectional Survey Data

      • Ding-Geng (Din) Chen, Xinguang (Jim) Chen, Feng Lin

      Pages 81-94

3. Modelling Incomplete or Missing Data

   1. Front Matter

      Pages 95-95

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   2. On the Effects of Structural Zeros in Regression Models

      • Hua He, Wenjuan Wang, Ding-Geng (Din) Chen, Wan Tang

      Pages 97-115

   3. Modeling Based on Progressively Type-I Interval Censored Sample

      • Yu-Jau Lin, Nan Jiang, Y. L. Lio, Ding-Geng (Din) Chen

      Pages 117-151

   4. Techniques for Analyzing Incomplete Data in Public Health Research

      • Valerie Pare, Ofer Harel

      Pages 153-171

   5. A Continuous Latent Factor Model for Non-ignorable Missing Data

      • Jun Zhang, Mark Reiser

      Pages 173-199

4. Healthcare Research Models

   1. Front Matter

      Pages 201-201

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   2. Health Surveillance

      • Steven E. Rigdon, Ronald D. Fricker Jr.

      Pages 203-249

   3. Standardization and Decomposition Analysis: A Useful Analytical Method for Outcome Difference, Inequality and Disparity Studies

      • Jichuan Wang

      Pages 251-264

   4. Cusp Catastrophe Modeling in Medical and Health Research

      • Xinguang (Jim) Chen, Ding-Geng Chen

      Pages 265-290

   5. On Ranked Set Sampling Variation and Its Applications to Public Health Research

      • Hani Samawi, Robert Vogel

      Pages 291-313

   6. Weighted Multiple Testing Correction for Correlated Endpoints in Survival Data

      • Changchun Xie, Enas Ghulam, Aimin Chen, Kesheng Wang, Susan M. Pinney, Christopher Lindsell

      Pages 315-324

   7. Meta-Analytic Methods for Public Health Research

      • Yan Ma, Wei Zhang, Ding-Geng Chen

      Pages 325-340

5. Back Matter

   Pages 341-351

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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.

• Causal inference
• Health surveillance
• Incomplete or missing data
• Public health statistics
• Standardization and decomposition analysis (SDA)
• Statistics biomedical 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)

• Wallace H. Kuralt Distinguished Professor, Director of Statistical Development and Consultation, School of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, USA

  Ding-Geng (Din) Chen

• Arizona State University, Tempe, USA

  Jeffrey Wilson

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.

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