Binbing Yu is a Senior Director in the Oncology Statistical Innovation group at AstraZeneca. He serves as the statistical expert across the whole spectrum of drug R&D, including drug discovery, clinical trials, operation and manufacturing, clinical pharmacology, oncology medical affairs and post-marketing surveillance. He obtained his PhD in Statistics from the George Washington University. His primary research interests are clinical trial design and analysis, cancer epidemiology, observational studies, PKPD modelling and Bayesian analysis. He has published three books on immunogenicity, cure modelling and RWD/RWE.Kristine Broglio is a Statistical Science Director in the Astrazeneca Oncology Statistical Innovation group with interests in adaptive clinical trials and Bayesian statistics. She earned an MS in Biostatistics from the University of Washington and joined the University of Texas M.D. Anderson Cancer Center where she specialized in applied statistical analysis relating to the diagnosis, treatment, and long-term outcomes of breast cancer. Later at Berry Consultants, she led the design, execution, and analysis of well over 100 Bayesian adaptive and complex clinical trials. Ms Broglio is a member of numerous cross-industry working groups through the ASA and DIA and has contributed to over 120 papers to the medical and statistical literature.

• 1. Review of Advances in Complex Innovative Clinical Trials Part 1. Case Studies in Adaptive Trial Design 2. ANBL1531: The Children’s Oncology Group (COG) Experience using a Bayesian Approach 3. Being SMART about Behavioral Intervention Trials for the Management of Chronic Conditions: Lessons Learned using Sequential Multiple Assignment Randomized Trials (SMARTs) 4. Adapting the Primary Endpoint of TULIP 2 – A Hybrid Bayesian-Frequentist Framework to Incorporate Relevant Information from Prior Studies in Confirmatory Trials in SLE Patients 5. Unblinded Sample Size Re-Estimation: A Case Study. 6. Evaluation of a Method for Sample Size Re-Estimation for a Confirmatory Phase 3 Clinical Trial to Compare Two Test Treatments to Control 7. Hierarchical Composite Endpoints in COVID-19: The DARE-19 Trial Part 2. Case Studies in Other Innovative Clinical Trial Methods 8. Deep Learning Constructed Statistics with Application to Adaptive Designs for Clinical Trials 9. Predicting Phase III Results by Incorporating Historical Data using Bayesian Additive Regression Trees (BART) Extensions 10. Unleashing the Power of Digital Tools in Clinical Trials: A Systematic Review of Digital Measurement Considerations from Implementation Experience 11. Use of Surrogate Endpoints in Clinical Development 12. Advanced Clinical Trial Design that Utilizes Real-World Evidence Part 3. Case Studies in Regulatory and Operational Considerations 13. Case Studies in Statistical Safety 14. Bayesian Dynamic Borrowing and Regulatory Considerations 15. Case Studies in CID and Model-Informed Drug Design (MIDD) Part 4. Case Studies in Collaboration and Communication 16. Project Management in Innovative Clinical Trial Design

Binbing Yu is a Senior Director in the Oncology Statistical Innovation group at AstraZeneca. He serves as the statistical expert across the whole spectrum of drug R&D, including drug discovery, clinical trials, operation and manufacturing, clinical pharmacology, oncology medical affairs and post-marketing surveillance. He obtained his PhD in Statistics from the George Washington University. His primary research interests are clinical trial design and analysis, cancer epidemiology, observational studies, PKPD modelling and Bayesian analysis. He has published three books on immunogenicity, cure modelling and RWD/RWE.Kristine Broglio is a Statistical Science Director in the Astrazeneca Oncology Statistical Innovation group with interests in adaptive clinical trials and Bayesian statistics. She earned an MS in Biostatistics from the University of Washington and joined the University of Texas M.D. Anderson Cancer Center where she specialized in applied statistical analysis relating to the diagnosis, treatment, and long-term outcomes of breast cancer. Later at Berry Consultants, she led the design, execution, and analysis of well over 100 Bayesian adaptive and complex clinical trials. Ms Broglio is a member of numerous cross-industry working groups through the ASA and DIA and has contributed to over 120 papers to the medical and statistical literature.

• 1. Review of Advances in Complex Innovative Clinical Trials Part 1. Case Studies in Adaptive Trial Design 2. ANBL1531: The Children’s Oncology Group (COG) Experience using a Bayesian Approach 3. Being SMART about Behavioral Intervention Trials for the Management of Chronic Conditions: Lessons Learned using Sequential Multiple Assignment Randomized Trials (SMARTs) 4. Adapting the Primary Endpoint of TULIP 2 – A Hybrid Bayesian-Frequentist Framework to Incorporate Relevant Information from Prior Studies in Confirmatory Trials in SLE Patients 5. Unblinded Sample Size Re-Estimation: A Case Study. 6. Evaluation of a Method for Sample Size Re-Estimation for a Confirmatory Phase 3 Clinical Trial to Compare Two Test Treatments to Control 7. Hierarchical Composite Endpoints in COVID-19: The DARE-19 Trial Part 2. Case Studies in Other Innovative Clinical Trial Methods 8. Deep Learning Constructed Statistics with Application to Adaptive Designs for Clinical Trials 9. Predicting Phase III Results by Incorporating Historical Data using Bayesian Additive Regression Trees (BART) Extensions 10. Unleashing the Power of Digital Tools in Clinical Trials: A Systematic Review of Digital Measurement Considerations from Implementation Experience 11. Use of Surrogate Endpoints in Clinical Development 12. Advanced Clinical Trial Design that Utilizes Real-World Evidence Part 3. Case Studies in Regulatory and Operational Considerations 13. Case Studies in Statistical Safety 14. Bayesian Dynamic Borrowing and Regulatory Considerations 15. Case Studies in CID and Model-Informed Drug Design (MIDD) Part 4. Case Studies in Collaboration and Communication 16. Project Management in Innovative Clinical Trial Design

Binbing Yu is a Senior Director in the Oncology Statistical Innovation group at AstraZeneca. He serves as the statistical expert across the whole spectrum of drug R&D, including drug discovery, clinical trials, operation and manufacturing, clinical pharmacology, oncology medical affairs and post-marketing surveillance. He obtained his PhD in Statistics from the George Washington University. His primary research interests are clinical trial design and analysis, cancer epidemiology, observational studies, PKPD modelling and Bayesian analysis. He has published three books on immunogenicity, cure modelling and RWD/RWE.Kristine Broglio is a Statistical Science Director in the Astrazeneca Oncology Statistical Innovation group with interests in adaptive clinical trials and Bayesian statistics. She earned an MS in Biostatistics from the University of Washington and joined the University of Texas M.D. Anderson Cancer Center where she specialized in applied statistical analysis relating to the diagnosis, treatment, and long-term outcomes of breast cancer. Later at Berry Consultants, she led the design, execution, and analysis of well over 100 Bayesian adaptive and complex clinical trials. Ms Broglio is a member of numerous cross-industry working groups through the ASA and DIA and has contributed to over 120 papers to the medical and statistical literature.

• 1. Review of Advances in Complex Innovative Clinical Trials Part 1. Case Studies in Adaptive Trial Design 2. ANBL1531: The Children’s Oncology Group (COG) Experience using a Bayesian Approach 3. Being SMART about Behavioral Intervention Trials for the Management of Chronic Conditions: Lessons Learned using Sequential Multiple Assignment Randomized Trials (SMARTs) 4. Adapting the Primary Endpoint of TULIP 2 – A Hybrid Bayesian-Frequentist Framework to Incorporate Relevant Information from Prior Studies in Confirmatory Trials in SLE Patients 5. Unblinded Sample Size Re-Estimation: A Case Study. 6. Evaluation of a Method for Sample Size Re-Estimation for a Confirmatory Phase 3 Clinical Trial to Compare Two Test Treatments to Control 7. Hierarchical Composite Endpoints in COVID-19: The DARE-19 Trial Part 2. Case Studies in Other Innovative Clinical Trial Methods 8. Deep Learning Constructed Statistics with Application to Adaptive Designs for Clinical Trials 9. Predicting Phase III Results by Incorporating Historical Data using Bayesian Additive Regression Trees (BART) Extensions 10. Unleashing the Power of Digital Tools in Clinical Trials: A Systematic Review of Digital Measurement Considerations from Implementation Experience 11. Use of Surrogate Endpoints in Clinical Development 12. Advanced Clinical Trial Design that Utilizes Real-World Evidence Part 3. Case Studies in Regulatory and Operational Considerations 13. Case Studies in Statistical Safety 14. Bayesian Dynamic Borrowing and Regulatory Considerations 15. Case Studies in CID and Model-Informed Drug Design (MIDD) Part 4. Case Studies in Collaboration and Communication 16. Project Management in Innovative Clinical Trial Design