Details
Modern Statistical Methods for Health Research
Emerging Topics in Statistics and Biostatistics
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139,09 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 14.10.2021 |
| ISBN/EAN: | 9783030724375 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<p>This book brings together the voices of leading experts in the frontiers of biostatistics, biomedicine, and the health sciences to discuss the statistical procedures, useful methods, and novel applications in biostatistics research. It also includes discussions of potential future directions of biomedicine and new statistical developments for health research, with the intent of stimulating research and fostering the interactions of scholars across health research related disciplines. Topics covered include:</p>
<p> Health data analysis and applications to EHR data</p>
<p> Clinical trials, FDR, and applications in health science</p>
<p> Big network analytics and its applications in GWAS</p>
<p> Survival analysis and functional data analysis</p><p> Graphical modelling in genomic studies<br></p>
<p>The book will be valuable to data scientists and statisticians who are working in biomedicine and health, other practitioners in the health sciences, and graduate students and researchers in biostatistics and health.<b></b></p><br><p></p>
<p> Health data analysis and applications to EHR data</p>
<p> Clinical trials, FDR, and applications in health science</p>
<p> Big network analytics and its applications in GWAS</p>
<p> Survival analysis and functional data analysis</p><p> Graphical modelling in genomic studies<br></p>
<p>The book will be valuable to data scientists and statisticians who are working in biomedicine and health, other practitioners in the health sciences, and graduate students and researchers in biostatistics and health.<b></b></p><br><p></p>
1. Alternative Capture-Recapture Point and Interval Estimators Based on Two Surveillance Streams – Lyles, Wilkinson, Williamson, Chen, Taylor, Jambai, Kaiser.- 2. On-Gaussian Model Based Object Tracking Analysis with Time Lapse Fluorescence Microscopy Images – Marcus, Kong.- 3. Detecting Changepoint in Gene Expressions over Time: An Application to Childhood Obesity – Mathur, Sung.- 4. How “Big” Are EHR Data? The Effective Sample Size of EHR Data Under Biased Sampling – Hubbard.- 5. A Nested Clustering Method to Detect and Cluster Transgenerational DNA Methylation Sites via Beta Regressions – Wang, Zhang, Han, Arshad, Karmaus.- 6. Controlling the False Discovery Rate of Grouped Hypotheses – MacDonald, Wilson, Liang, Qin.- 7. Approaches to Combining Phase II Proof-of-Concept and Dose-Finding Trials – Ting.- 8. On the Multiply Robust Estimation with Missing Data – Chen, Haziza.- 9. Recent Advances in Spectral Clustering and Their Applications in Bioinformatics – Xue.- 10. Functional DataModeling and Hypothesis Testing for Longitudinal Alzheimer Genome-Wide Association Studies – Li, Xu, Liu.- 11. Misuse of Classifiers in Biological Networks – Maharaj.- 12. A Selective Inference-based Two-stage Procedure for Clinical Safety Studies – Zhu, Guo.- 13. Inferring Stage of HCV Infections as Recent or Chronic by Machine Learning approach – Icer.- 14. Graphical Modeling of Multiple Biological Pathways in Genomic Studies – Cao, Zhang, Chen, Wang.- 15. Online Updating of Nonparametric Survival Estimator and Nonparametric Survival Test – Xue, Schifano, Hu.- 16. Mixed-Effects Negative Binomial Regression with Interval Censoring: A Simulation Study and Application to Precipitation and All-Cause Mortality Rates among Black South Africans over 1997-2013 – Landon, Lyles, Scovronick, Abadi, Bilotta, Hauer, Bell, Gribble.- 17. SAS Macros for Linear Mediation Analysis of Complex Survey Data Using Balanced Repeated Replication – Mai, Zhang.- 18. Joint Modeling of Multiple Skewed Longitudinal Processes with Excess of Zero and Time-to-Event: An Application to Fecundity Studies – Mirzaei, Kundu, Sundaram.- 19. Infectious Disease Epidemiology: Forecasting the Ongoing 2018-19 Ebola Epidemic in the Democratic Republic of Congo (DRC) Using Phenomenological Growth Models – Tariq, Chowell.- 20. Models and Estimation Methods for Item Factor Analysis: An Overview – Chen, Zhang.
<p><b>Professor Yichuan Zhao </b>is a professor of statistics at Georgia State University in Atlanta. He has a joint appointment as associate member of the Neuroscience Institute, and he is also an affiliated faculty member of the School of Public Health at Georgia State University. His current research interest focuses on survival analysis, empirical likelihood methods, nonparametric statistics, analysis of ROC curves, bioinformatics, Monte Carlo methods, and statistical modelling of fuzzy systems. He has published 100 research articles in statistics and biostatistics, has co-edited four books on statistics, biostatistics and data science, and has been invited to deliver more than 200 research talks nationally and internationally. Dr. Zhao has organized the Workshop Series on Biostatistics and Bioinformatics since its initiation in 2012. He also organized the 25th ICSA Applied Statistics Symposium in Atlanta as a chair of the organizing committee to great success. He is currently serving as associate editor, or on the editorial board, for several statistical journals. Dr. Zhao is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and serves on the Board of Directors, ICSA.<b> </b></p>
<p></p><p><b>Professor (Din) Ding-Geng Chen </b>is a fellow of the American Statistical Association. He is an honorary professor at the School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, South Africa and an extraordinary professor at the Department of Statistics, University of Pretoria, South Africa. Professor Chen has written more than 200 refereed publications and co-authored/co-edited 33 books on clinical trial methodology, meta-analysis, causal inference and public health statistics. This work is partially supported by the National Research Foundation of South Africa (Grant Number 127727) and the South African National Research Foundation (NRF) and South African Medical Research Council(SAMRC) (South African DST-NRF-SAMRC SARChI Research Chair in Biostatistics, Grant Number 114613).</p><br><p></p>
<p></p><p><b>Professor (Din) Ding-Geng Chen </b>is a fellow of the American Statistical Association. He is an honorary professor at the School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, South Africa and an extraordinary professor at the Department of Statistics, University of Pretoria, South Africa. Professor Chen has written more than 200 refereed publications and co-authored/co-edited 33 books on clinical trial methodology, meta-analysis, causal inference and public health statistics. This work is partially supported by the National Research Foundation of South Africa (Grant Number 127727) and the South African National Research Foundation (NRF) and South African Medical Research Council(SAMRC) (South African DST-NRF-SAMRC SARChI Research Chair in Biostatistics, Grant Number 114613).</p><br><p></p>
<p>This book brings together the voices of leading experts in the frontiers of biostatistics, biomedicine, and the health sciences to discuss the statistical procedures, useful methods, and novel applications in biostatistics research. It also includes discussions of potential future directions of biomedicine and new statistical developments for health research, with the intent of stimulating research and fostering the interactions of scholars across health research related disciplines. Topics covered include:</p>
<p><ul><li> Health data analysis and applications to EHR data</li><li> Clinical trials, FDR, and applications in health science</li><li> Big network analytics and its applications in GWAS</li><li> Survival analysis and functional data analysis</li><li> Graphical modelling in genomic studies </li></ul></p>
<p>The book will be valuable to data scientists and statisticians who are working in biomedicine and health, other practitioners in the health sciences, and graduate students and researchers in biostatistics and health.<b></b></p>
<p><ul><li> Health data analysis and applications to EHR data</li><li> Clinical trials, FDR, and applications in health science</li><li> Big network analytics and its applications in GWAS</li><li> Survival analysis and functional data analysis</li><li> Graphical modelling in genomic studies </li></ul></p>
<p>The book will be valuable to data scientists and statisticians who are working in biomedicine and health, other practitioners in the health sciences, and graduate students and researchers in biostatistics and health.<b></b></p>
Features the significant contributions of many leading statisticians and data scientists to contemporary health sciences Includes a comprehensive review of statistical methods and new approaches in health research to guide the reader in learning efficiently Covers new developments in EHR data analysis, clinical trials, FDR, big network analytics, applications in GWAS, graphical modeling in genomic studies, survival analysis, and functional data analysis Offers innovative statistical procedures, computer program, new data sets, and novel applications in modern health research
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