Biostatistics in Biopharmaceutical Research and Development

Dr. Din Chen is a Fellow of the American Statistical Association and is currently the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. He is also an Extraordinary Professor and the SARChI in biostatistics at the University of Pretoria, and an honorary professor at the University of KwaZulu-Natal, South Africa. He is a senior biostatistics consultant for biopharmaceuticals and government agencies with extensive expertise in biostatistics, clinical trials, and public health statistics. Dr. Chen has more than 200 referred professional publications and co-authored 11 books and co-edited 24 books on clinical trial methodology, meta-analysis, data science, causal inference, and public health research. Professor Chen is the Deming publicity chair and he has been honored with the "Award of Recognition" from the Deming Conference Committee for highly successful advanced biostatistics workshop tutorials with his books.

• Bias and Randomization in Clinical Trials: 1980s – 2020s – 2060s.- The Markov Model for Survival Trials at 35 Years-Old.- Absolute Power Corrupts Absolutely: A Review of the Use of Unconditional Probabilities in the Planning of Clinical Trials.- Design of Clinical Trials with the Desirability of Outcome Ranking Methodology.- Benefit:Risk Assessments during Clinical Trials: A Prediction Approach Using the Desirability of Outcome Ranking (DOOR).- The Power of Integration: How the 2-in-1 Clinical Trial Design is Changing the Future of Drug Development.- A Unified Bayesian Decision Rule-Based Approach for Bayesian Design of Clinical Trials Using Historical Data.- Group Sequential Design Under Non-proportional Hazards: Methodologies and Examples.- Multiple Testing in Group Sequential Design.- Plan per-protocol (PP) causal inference analysis addressing intercurrent events following the targeted learning roadmap.- Maximum Tolerated Imbalance Randomization: Theory and Practice.- Response-adaptive randomization designs based on optimal allocation proportion.