Statistics in Precision Health

Theory, Methods and Applications
Herausgegeben:Zhao, Yichuan; Chen, Ding-Geng

• Gebundenes Buch

Produktbeschreibung

This book discusses statistical methods and their innovative applications in precision health. It serves as a valuable resource to foster the development of this growing field within the context of the big data era. The chapters cover a wide range of topics, including foundational principles, statistical theories, new procedures, advanced methods, and practical applications in precision medicine. Particular attention is devoted to the interplay between precision health, big data, and mobile health research, while also exploring precision medicine's role in clinical trials, electronic health record data analysis, survival analysis, and genomic studies. Targeted at data scientists, statisticians, graduate students, and researchers in academia, industry, and government, this book offers insights into the latest advances in personalized medicine using advanced statistical techniques.

Produktdetails

• ICSA Book Series in Statistics
• Verlag: National Research Foundation of South Africa / South African DST-NRF-SAMRC SARChI Research Chair in
• Artikelnr. des Verlages: 978-3-031-50689-5
• 2024
• Seitenzahl: 564
• Erscheinungstermin: 10. Juli 2024
• Englisch
• Abmessung: 241mm x 160mm x 36mm
• Gewicht: 947g
• ISBN-13: 9783031506895
• ISBN-10: 3031506898
• Artikelnr.: 69447345

Autorenporträt

Dr. Yichuan Zhao 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 more than 100 research articles in statistics and biostatistics, has co-edited six 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 the chair of the organizing committee to great success. In addition, the 6th ICSA China Conference that he organized as the chair of both the organizing committee and program committee was a huge 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. Dr. Ding-Geng Chen is a fellow of the American Statistical Association and is currently the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. He is also an extraordinary professor and the SARChI in biostatistics at the University of Pretoria, an honorary professor at the University of KwaZulu-Natal, South Africa. Dr. Chen was a professor in Biostatistics at the University of North Carolina at Chapel Hill, a professor in biostatistics at the University of Rochester Medical School, and the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia Southern University. He is a senior biostatistics consultant for biopharmaceuticals and government agencies with extensive expertise in biostatistics, clinical trials, and public health statistics. Dr. Chen has more than 200 referred professional publications, co-authored 11 books and co-edited 24 books on clinical trial methodology, meta-analysis, data science, causal inference, and public health research. 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).

Inhaltsangabe

Part I An Overview of Precision Health in the Big Data Era.- Overview of Precision Health: Past, Current, and Future.- A Selective Review of Individualized Decision Making.- Utilizing Wearable Devices to Improve Precision in Physical Activity Epidemiology: Sensors, Data and Analytic Methods.- Policy Learning for Individualized Treatment Regimes on Infinite Time Horizon.- Q-Learning Based Methods for Dynamic Treatment Regimes.- Personalized Medicine with Multiple Treatments.- Statistical Reinforcement Learning and Dynamic Treatment Regimes.- Part II New Advances in Statistical Methods of Precision Medicine and the Applications.- Integrative Learning to Combine Individualized Treatment Rules from Multiple Randomized Trials.- Adaptive Semi-supervised Learning for Optimal Treatment Regime Estimation with Application to EMR Data.- Estimation and Inference for Individualized Treatment Rules Using Efficient Augmentation and Relaxation Learning.- Subgroup Analysis Using Doubly Robust Semiparametric Procedures.- A Selective Overview of Fusion Penalized Learning in Latent Subgroup Analysis for Precision Medicine.- Part III Precision Medicine in Clinic Trials and the applications to EHR Data.- Mining for Health: A Comparison of Word Embedding Methods for Analysis of EHRs Data.- Adaptive Designs for Precision Medicine in Clinical Trials: A Review and Some Innovative Designs.- Maximum Likelihood Estimation and Design and Inference Considerations for Sequential Multiple Assignment Randomized Trials.- Precision Medicine Designs for Cancer Clinical Trials.- Part IV Precision Medicine in Survival Analysis and Genomic Studies.- Variant Selection and Aggregation of Genetic Association Studies in Precision Medicine.- Leveraging Functional Annotations Improves Cross-population Genetic Risk Prediction.- A Soft-Thresholding Operator for Sparse Time-Varying Effects in Survival Models.- Discovery of Gene-specific Time Effects on Survival.- Modeling and Optimizing Dynamic Treatment Regimens in Continuous Time.

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Part I An Overview of Precision Health in the Big Data Era.- Overview of Precision Health: Past, Current, and Future.- A Selective Review of Individualized Decision Making.- Utilizing Wearable Devices to Improve Precision in Physical Activity Epidemiology: Sensors, Data and Analytic Methods.- Policy Learning for Individualized Treatment Regimes on Infinite Time Horizon.- Q-Learning Based Methods for Dynamic Treatment Regimes.- Personalized Medicine with Multiple Treatments.- Statistical Reinforcement Learning and Dynamic Treatment Regimes.- Part II New Advances in Statistical Methods of Precision Medicine and the Applications.- Integrative Learning to Combine Individualized Treatment Rules from Multiple Randomized Trials.- Adaptive Semi-supervised Learning for Optimal Treatment Regime Estimation with Application to EMR Data.- Estimation and Inference for Individualized Treatment Rules Using Efficient Augmentation and Relaxation Learning.- Subgroup Analysis Using Doubly Robust Semiparametric Procedures.- A Selective Overview of Fusion Penalized Learning in Latent Subgroup Analysis for Precision Medicine.- Part III Precision Medicine in Clinic Trials and the applications to EHR Data.- Mining for Health: A Comparison of Word Embedding Methods for Analysis of EHRs Data.- Adaptive Designs for Precision Medicine in Clinical Trials: A Review and Some Innovative Designs.- Maximum Likelihood Estimation and Design and Inference Considerations for Sequential Multiple Assignment Randomized Trials.- Precision Medicine Designs for Cancer Clinical Trials.- Part IV Precision Medicine in Survival Analysis and Genomic Studies.- Variant Selection and Aggregation of Genetic Association Studies in Precision Medicine.- Leveraging Functional Annotations Improves Cross-population Genetic Risk Prediction.- A Soft-Thresholding Operator for Sparse Time-Varying Effects in Survival Models.- Discovery of Gene-specific Time Effects on Survival.- Modeling and Optimizing Dynamic Treatment Regimens in Continuous Time.