Medical Product Safety Evaluation : Biological Models and Statistical Methods / editors, Joseph Heyse, Tze Leung Lai.
"Medical Product Safety Evaluation: Biological Models and Statistical Methods presents cutting-edge biological models and statistical methods that are tailored to specific objectives and data types for safety analysis and benefit-risk assessment. Some frequently encountered issues and challenge...
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Full Text (via Taylor & Francis) |
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| Main Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton, FL :
CRC Press,
2018.
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| Edition: | First edition. |
| Series: | Chapman & Hall/CRC biostatistics series.
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| Subjects: |
MARC
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| 245 | 1 | 0 | |a Medical Product Safety Evaluation : |b Biological Models and Statistical Methods / |c editors, Joseph Heyse, Tze Leung Lai. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Boca Raton, FL : |b CRC Press, |c 2018. | |
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| 545 | 0 | |a Jie Chen is a distinguished scientist at Merck Research Laboratories. He has more than 20 years of experience in biopharmaceutical R & D with research interest in the areas of innovative trial design, data analysis, Bayesian methods, multiregional clinical trials, data mining and machining learning methods, and medical product safety evaluation. Joseph F. Heyse is a Scientific Assistant Vice President at Merck Research Laboratories, Fellow of the ASA and AAAS, and founding editor of Statistics in Biopharmaceutical Research. He has more than 40 years of experience in pharmaceutical R & D with research interest in safety evaluation and health economics and has more than 70 publications in peer reviewed journals. He is an editor of Statistical Methods in Medical Research. Tze Leung Lai is the Ray Lyman Wilbur Professor of Statistics, and by courtesy, of Biomedical Data Science and Computational & Mathematical Engineering, and Co-director of the Center for Innovative Study Design at Stanford University. He is a Fellow of the IMS and ASA. His research interest includes sequential experimentation, adaptive design and control, change-point detection, survival analysis, time series and forecasting, multivariate analysis and machine learning, safety evaluation and monitoring. He has published 12 books and 300 articles in peer reviewed journals, and has supervised over 70 PhD theses at Columbia and Stanford Universities. | |
| 520 | 2 | |a "Medical Product Safety Evaluation: Biological Models and Statistical Methods presents cutting-edge biological models and statistical methods that are tailored to specific objectives and data types for safety analysis and benefit-risk assessment. Some frequently encountered issues and challenges in the design and analysis of safety studies are discussed with illustrative applications and examples. The book is designed not only for biopharmaceutical professionals, such as statisticians, safety specialists, pharmacovigilance experts, and pharmacoepidemiologists, who can use the book as self-learning materials or in short courses or training programs, but also for graduate students in statistics and biomedical data science for a one-semester course. Each chapter provides supplements and problems as more readings and exercises. Jie Chen is a distinguished scientist at Merck Research Laboratories. He has more than 20 years of experience in biopharmaceutical R & D with research interest in the areas of innovative trial design, data analysis, Bayesian methods, multiregional clinical trials, data mining and machining learning methods, and medical product safety evaluation. Joseph F. Heyse is a Scientific Assistant Vice President at Merck Research Laboratories, Fellow of the ASA and AAAS, and founding editor of Statistics in Biopharmaceutical Research. He has more than 40 years of experience in pharmaceutical R & D with research interest in safety evaluation and health economics and has more than 70 publications in peer reviewed journals. He is an editor of Statistical Methods in Medical Research. Tze Leung Lai is the Ray Lyman Wilbur Professor of Statistics, and by courtesy, of Biomedical Data Science and Computational & Mathematical Engineering, and Co-director of the Center for Innovative Study Design at Stanford University. He is a Fellow of the IMS and ASA. His research interest includes sequential experimentation, adaptive design and control, change-point detection, survival analysis, time series and forecasting, multivariate analysis and machine learning, safety evaluation and monitoring. He has published 12 books and 300 articles in peer reviewed journals, and has supervised over 70 PhD theses at Columbia and Stanford Universities."--Provided by publisher. | |
| 505 | 0 | |a Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; List of Figures; List of Tables; 1: Introduction; 1.1 Expecting the unexpected; 1.1.1 A brief history of medical product regulation; 1.1.2 Science of safety; 1.1.3 Differences and similarities between efficacy and safety endpoints; 1.1.4 Regulatory guidelines and drug withdrawals; 1.2 Adverse events and adverse drug reactions; 1.2.1 Adverse events versus adverse drug reactions; 1.2.2 Safety data coding; 1.3 Drug dictionaries; 1.3.1 WHO Drug Dictionary; 1.3.2 Anatomical-Therapeutic-Chemical classification | |
| 505 | 8 | |a 1.3.3 NCI Drug Dictionary1.4 Adverse event dictionaries; 1.4.1 Medical Dictionary for Regulatory Activities; 1.4.2 Common Terminology Criteria for Adverse Events; 1.4.3 WHO's Adverse Reaction Terminology; 1.4.4 ICD and COSTART; 1.5 Serious adverse events and safety signals; 1.6 Statistical strategies for safety evaluation and a road map for readers; 1.6.1 Safety data collection and analysis; 1.6.2 Safety databases and sequential surveillance in pharmacovigilance; 1.6.3 An interdisciplinary approach and how the book can be used; 1.7 Supplements and problems | |
| 505 | 8 | |a 2: Biological Models and Associated Statistical Methods2.1 Quantitative structure-activity relationship; 2.1.1 Toxicity endpoints; 2.1.2 Molecular descriptors; 2.1.3 Statistical methods; 2.1.4 Model validation; 2.2 Pharmacokinetic-pharmacodynamic models; 2.3 Analysis of preclinical safety data; 2.3.1 Carcinogenicity; 2.3.2 Reproductive and developmental toxicity; 2.4 Predictive cardiotoxicity; 2.4.1 Comprehensive in vitro Proarrythmia Assay (CiPA); 2.4.2 Phase I ECG studies; 2.4.3 Concentration-QTc (C-QTc) modeling; 2.5 Toxicogenomics in predictive toxicology; 2.5.1 TGx science and technology | |
| 505 | 8 | |a 2.5.2 TGx biomarkers2.6 Regulatory framework in predictive toxicology; 2.6.1 Regulatory guidelines; 2.6.2 Safety biomarker qualification; 2.6.3 In silico models in predictive toxicology; 2.7 Supplements and problems; 3: Benefit-Risk Assessment of Medical Products; 3.1 Some examples of B-R assessments; 3.1.1 Tysabri; 3.1.2 Lorcaserin; 3.1.3 Crizotinib; 3.2 Ingredients for B-R evaluation; 3.2.1 Planning process; 3.2.2 Qualitative and quantitative evaluations; 3.2.3 Benefit-risk formulations; 3.3 B-R methods using clinical trials data; 3.4 Multi-criteria statistical decision theory | |
| 505 | 8 | |a 3.4.1 Multi-criteria decision analysis3.4.2 Stochastic multi-criteria acceptability analysis and statistical decision theory; 3.5 Quality-adjusted benefit-risk assessments; 3.5.1 Q-TWiST; 3.5.2 Quality-adjusted survival analysis; 3.5.3 Testing QAL differences of treatment from control; 3.6 Additional statistical methods; 3.6.1 Number needed to treat (NNT); 3.6.2 Incremental net benefits; 3.6.3 Uncertainty adjustments and Bayesian methods; 3.6.4 Endpoint selection and other considerations; 3.7 Supplements and problems; 4: Design and Analysis of Clinical Trials with Safety Endpoints | |
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