Theory, Methods and Applications Zhao, Yichuan. editor. edt http://id.loc.gov/vocabulary/relators/edt Chen, Ding-Geng. editor. edt http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XVII, 544 p. 95 illus., 80 illus. in color. online resource. 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. 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. edited by Yichuan Zhao, Ding-Geng Chen. Biometry Quantitative research Medicine Biostatistics Data Analysis and Big Data Clinical Medicine QH323.5 9783031506901 https://doi.org/10.1007/978-3-031-50690-1 https://doi.org/10.1007/978-3-031-50690-1 240709 20251030112619.0 978-3-031-50690-1