"Clinical Trials: Data Management and Quality Control" is a comprehensive guide that delves into the critical aspects of managing and ensuring the quality of data in clinical research. It begins with an introduction to data management, highlighting its importance and historical development, and covers essential topics such as designing and implementing data management plans, creating effective case report forms (CRFs), and selecting electronic data capture (EDC) systems. The book provides detailed insights into data entry, verification, cleaning, and validation processes, and explores the principles of database design and management. It emphasizes the importance of data quality assurance (DQA), monitoring, and auditing, and addresses the handling of data discrepancies, privacy, and security. Regulatory compliance with frameworks such as FDA, EMA, and GCP, along with the significance of training and education in data management, are thoroughly examined. The book also discusses risk-based data management, the impact of emerging technologies like AI and machine learning, and the challenges of managing data in multinational trials. It integrates patient-reported outcomes (PROs) and real-world data (RWD) into clinical trials, presenting case studies and future perspectives to guide researchers in navigating the evolving landscape of clinical data management.