Ulteriori informazioni
The subject of this book is applied Bayesian methods for chemistry, manufacturing, and control (CMC) studies in the biopharmaceutical industry. The book has multiple authors from industry and academia, each contributing a case study (chapter). The collection of case studies covers a broad array of CMC topics, including stability analysis, analytical method development, specification setting, process development and optimization, process control, experimental design, dissolution testing, and comparability studies. The analysis of each case study includes a presentation of code and reproducible output. This book is written with an academic level aimed at practicing nonclinical biostatisticians, most of whom have graduate degrees in statistics.
- First book of its kind focusing strictly on CMC Bayesian case studies
- Case studies with code and output
- Representation from several companies across the industry as well as academia
- Authors are leading and well-known Bayesian statisticians in the CMC field
- Accompanying website with code for reproducibility
- Reflective of real-life industry applications/problems
Sommario
1. Introduction 2. An Overview of Bayesian Computation 3. Basic Bayesian Model Checking 4. Quantitative Decision - Making, a CMC application to analytical method equivalence 5. Bayesian Dissolution Testing 6. A Non-Normal Bayesian Model for the Estimation and Comparison of Immunogenicity Screening Assay Cut-Points 7. Application of Bayesian Hierarchical Models to Experimental Design 8. Bayesian Prediction for Staged Testing Procedures 9. A Bayesian Approach to Multivariate Conditional Regression Surrogate Modeling with Application to Real Time Release Testing 10. Bayesian Approach for Demonstrating Analytical Similarity 11. Bayesian Evaluation and Monitoring of Process Comparability 12. Bayesian Alternatives to Traditional Methods for Estimating Product Shelf Life and Internal Release Limits 13. Application of Bayesian Methods for Specification Setting 14. Calculating Statistical Tolerance Intervals Using SAS 15. A Bayesian Application in Process Monitoring - Establishing Limits for Dosage Units in Early Phase Process Control
Info autore
Paul Faya (Ph.D.) is a Director in Discovery and Development Statistics with Eli Lilly and Company, USA.
Tony Pourmohamad (Ph.D.) is a Principal Statistical Scientist with Genentech, USA, and an Assistant Adjunct Professor in the Department of Statistics at the University of California, Santa Cruz.
Riassunto
The subject of this book is applied Bayesian methods for chemistry, manufacturing, and control (CMC) studies in the biopharmaceutical industry. The book has multiple authors from industry and academia, each contributing a case study (chapter), covering a broad array of CMC topics.
