Clinical Data Management, Biostatistics & AI Applications

This session will cover essential aspects of data handling, analysis, and interpretation in clinical trials. Topics include data collection, database design, data cleaning, and validation processes. Subtopics explore statistical analysis techniques, risk-based monitoring, interim analysis, and adaptive trial data interpretation. The session will also highlight emerging technologies such as artificial intelligence, machine learning, and predictive analytics to enhance data accuracy, detect anomalies, and optimize trial efficiency. Case studies will illustrate real-world applications of AI in monitoring patient outcomes, forecasting recruitment trends, and analyzing complex datasets. Discussions will cover regulatory requirements for electronic data capture, data privacy, and secure handling of sensitive patient information. Attendees will learn strategies to integrate advanced analytics with traditional statistical methods, ensuring robust, reliable, and reproducible results. By the end of the session, participants will gain insights into leveraging modern data tools to improve trial decision-making, enhance regulatory compliance, and accelerate clinical research outcomes.

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