Data Management and Statistical Analysis

Data management and statistical analysis

Gábor Kovács
University of Pécs, Hungary

The primary objective is to comprehend the significance of Clinical Data Management in clinical research, as it contributes to the production of high-quality and dependable data from clinical trials, thereby enhancing its reliability for future sharing and reuse. Given that data management is an essential component of open research, CONSCIOUS II aims to emphasize this topic. We provide a comprehensive examination of the data flow process, starting from its source and concluding with a usable dataset for analysis at the Sponsor site. Additionally, we explore the benefits and drawbacks of utilizing paper CRF (pCRF) and electronic CRF (eCRF) for data capture.

Chapter 6 focuses on the core principles and standards of research data management, as outlined by the FAIR Principles and their global implementation. Data management (DM) encompasses a series of procedures involved in handling information during medical research. Its primary objective is to ensure the quality, integrity, and adherence to various protocols and regulations. Additionally, it assists key stakeholders, such as sponsors, research organizations, and clinical centers where data collection occurs, in staying on track. Effective data management plays a crucial role in guaranteeing the production of high-quality data, reports, validation, and a well-executed clinical trial. Effective data management means understanding where the data is, and the ability to get the data into some form where it can be appropriately managed.


Chapter 6- Data Management and Statistical Analysis (University of Pécs, Pécs, Hungary)

Final chapter

Chapter 6- Data Management and Statistical Analysis (University of Pécs, Pécs, Hungary)