This book presents the proceedings of the 39th annual Midwest Biopharmaceutical Statistics Workshop (MBSW), held in Muncie, Indiana on May 16-18, 2016. It consists of selected peer- reviewed and revised papers on topics ranging from statistical applications in drug discovery and CMC to biomarkers, clinical trials, and statistical programming. All contributions feature original research, and together they cover the full spectrum of pharmaceutical R&D - with a special focus on emergent topics such as biosimilarity, bioequivalence, clinical trial design, and subgroup identification.
Founded in 1978, the MBSW has provided a forum for statisticians to share knowledge, research, and applications on key statistical topics in pharmaceutical R&D for almost forty years, with the 2016 conference theme being "The Power and 3 I's of Statistics: Innovation, Impact and Integrity." The papers gathered here will be of interest to all researchers whose work involves the quantitative aspects of pharmaceutical research and development, including pharmaceutical statisticians who want to keep up-to-date with the latest trends, as well as academic statistics researchers looking for areas of application.
Autorentext
Ray Liu, PhD, is the Head of Statistical Innovation and Consultation at Takeda Pharmaceutical Company. Dr. Liu received his BS and MA degrees from the National Taiwan University, Taiwan and his PhD from Columbia University, USA. He currently oversees the development of novel methodologies and provides statistical consultation and project support to various functional areas in pharmaceutical R&D, including Discovery, CMC, Translational Research, and Outcome Research. Dr. Liu was the conference chair of the 38th and 39th Midwest Biopharmaceutical Statistics Workshop. He is the author of more than 30 statistical and scientific articles and book chapters, editor of the Springer book Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics, frequent conference speaker on Big Data and the member of the ASA, PSTC Stats working group, IQ consortium, and NCBLF.
Yi Tsong, PhD, is the Division Director of Biometrics at the Food and Drug Administration (FDA). He specializes in post-marketing risk assessment, drug manufacturing process control and quality assurance, active control non-inferiority/equivalence tests, adaptive design, bioequivalence and analytical biosimilarity, and QTc trials, and has published more than 90 articles in these areas. Dr. Tsong received his PhD in Statistics from the University of North Carolina at Chapel Hill, USA and did his post-doctoral training in cardiovascular prevention and biostatistics at Northwestern University Medical School, USA. He has served as the Treasurer, Board Director, and President of the International Chinese Statistical Association and currently serves as Associate Editor of Statistics in Medicine, *the *Journal of Biopharmaceutical Statistics,*and *Pharmaceutical Statistics.
Inhalt
Part I: Specification and Sampling Acceptance Tests.- Chapter 1. Statistical Considerations in Setting Quality Specification Limits using Quality Data (Yi Tsong).- Chapter 2. Counting Test and Parametric Two One-sided Tolerance Interval Test for Content Uniformity using Large Sample Sizes (Meiyu Shen).- Part II: Analytical Biosimilar and Process Validation.- Chapter 3. Sample Size Consideration for Equivalent Test of Tier-1 Quality Attributes for Analytical Biosimilarity Assessment (Tianhua Wang).- Chapter 4. A Probability Based Equivalence Test of NIR vs HPLC Analytical Methods in a Continuous Manufacturing Process Validation Study (Areti Manola).- Chapter 5. A Further Look at the Current Equivalence Test for Analytical Similarity Assessment (Neal Thomas).- Chapter 6. Shiny Tools for Sample Size Calculation in Process Performance Qualification of Large Molecules (Qianqiu Li).- Part III: Continuous Process.- Chapter 7. Risk Evaluation of Registered Specifications and Internal Release Limits Using a Bayesian Approach (Yijie Dong).- Chapter 8. Development of Statistical Computational Tools through Pharmaceutical Drug Development and Manufacturing Life Cycle (Fasheng Li).- Chapter 9. Application of Advanced Statistical Tools to Achieve Continuous Analytical Verification: A Risk Assessment Case of the Impact of Analytical Method Performance on Process Performance using a Bayesian Approach (Iris Yan).- Part IV: Clinical Trial Design and Analysis.- Chapter 10. Exact Inference for Adaptive Group Sequential Designs (Cyrus Mehta).- Chapter 11. A Novel Framework for Bayesian Response-adaptive Randomization (Jian Zhu).- Chapter 12. Sample Size Determination under Non-Proportional Hazards (Miao Yang).- Chapter 13. Adaptive Three-Stage Clinical Trial Design for a Binary Endpoint in the Rare Disease Setting (Lingrui Gan).- Part V: Biomarker-Driven Trial Design.- Chapter 14. Clinical Trial Designs to Evaluate Predictive Biomarkers: What's being Estimated? (Gene Pennello).- Chapter 15. Biomarker Enrichment Design Considerations in Oncology Single Arm Studies (Hong Tian).- Chapter 16. Challenges of Bridging Studies in Biomarker Driven Clinical Trials: The Impact of Companion Diagnostic Device Performance on Clinical Efficacy (Szu-Yu Tang).- Part VI: Application of Novel Data Modality.- Chapter 17. Parallel-Tempered Feature Allocation for Large-Scale Tumor Heterogeneity with Deep Sequencing Data (Yang Ni).- Chapter 18. Analysis of T-cell Immune Responses as Measured by Intracellular Cytokine Staining with Application to Vaccine Clinical Trials (Yunzhi Lin).- Chapter 19. Project Data Sphere and the Applications of Historical Patient Level Clinical Trial Data in Oncology Drug Development (Greg Hather).- Chapter 20. Novel Test for the Equality of Continuous Curves with Homoscedastic or Heteroscedastic Errors (Zhongfa Zhang).- Chapter 21. Quality Control Metrics for Extraction-free Targeted RNA-Seq under a Compositional Framework (Dominic LaRoche).- Part VII: Omics Data Analysis.- Chapter 22. Leveraging Omics Biomarker Data in Drug Development: with a GWAS Case Study (Weidong Zhang).- Chapter 23. A Simulation Study Comparing SNP Based Prediction Models of Drug Response (Wencan Zhang).