Statistics in Precision Health : Theory, Methods and Applications / edited by Yichuan Zhao, Ding-Geng Chen.

Format:

Book

Contributor:

Zhao, Yichuan, editor.

Chen, Ding-Geng, editor.

Series:

ICSA Book Series in Statistics, 2199-0999

Language:

English

Subjects (All):

Biometry.

Quantitative research.

Medicine.

Biostatistics.

Data Analysis and Big Data.

Clinical Medicine.

Local Subjects:

Biostatistics.

Data Analysis and Big Data.

Clinical Medicine.

Physical Description:

1 online resource (0 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2024.

Summary:

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.

Contents:

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.

Notes:

Includes bibliographical references and index.

ISBN:

3-031-50690-1