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E-raamat: Medical Product Safety Evaluation: Biological Models and Statistical Methods

(Merck & Co, Inc., North Wales, Pennsylvania, USA), (Stanford University, California, USA),
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Medical Product Safety Evaluation: Biological Models and Statistical MethodsSuurem pilt
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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.

Arvustused

"This book provides comprehensive coverage of the statistical methods for evaluating medical product safety in different stages of development life-cycle: from pre-clinical to clinical, and to post marketing studies. As the evaluation of safety of medical products including drugs, vaccines, devices are becoming increasingly important, more and more novel and complex statistical methods have recently been proposed and used. This book gives a very detailed account of the framework for safety evaluation as well as in-depth descriptions of many advanced statistical methods. As far as I am aware, it is the only book that covers such a broad arrays of topics in safety evaluation. Therefore, this book should be appealing to a very large audience, including graduate students and professional statisticians in industry, government, and academia. I think it can be used as a textbook (as many parts of the materials have been used for short courses or part of graduate degree course) or a reference book for practicing statisticians... Overall, I found the book to be a very important contribution to the scientific community." ~Ivan Chan, AbbVie "This book provides comprehensive coverage of the statistical methods for evaluating medical product safety in different stages of development life-cycle: from pre-clinical to clinical, and to post marketing studies. As the evaluation of safety of medical products including drugs, vaccines, devices are becoming increasingly important, more and more novel and complex statistical methods have recently been proposed and used. This book gives a very detailed account of the framework for safety evaluation as well as in-depth descriptions of many advanced statistical methods. As far as I am aware, it is the only book that covers such a broad arrays of topics in safety evaluation. Therefore, this book should be appealing to a very large audience, including graduate students and professional statisticians in industry, government, and academia. I think it can be used as a textbook (as many parts of the materials have been used for short courses or part of graduate degree course) or a reference book for practicing statisticians... Overall, I found the book to be a very important contribution to the scientific community." ~Ivan Chan, AbbVie

List of Figures
xiii
List of Tables
xv
Preface xvii
1 Introduction
1(24)
1.1 Expecting the unexpected
1(9)
1.1.1 A brief history of medical product regulation
3(3)
1.1.2 Science of safety
6(1)
1.1.3 Differences and similarities between efficacy and safety endpoints
7(1)
1.1.4 Regulatory guidelines and drug withdrawals
8(2)
1.2 Adverse events and adverse drug reactions
10(1)
1.2.1 Adverse events versus adverse drug reactions
10(1)
1.2.2 Safety data coding
11(1)
1.3 Drug dictionaries
11(2)
1.3.1 WHO Drug Dictionary
11(1)
1.3.2 Anatomical-Therapeutic-Chemical classification
12(1)
1.3.3 NCI Drug Dictionary
13(1)
1.4 Adverse event dictionaries
13(5)
1.4.1 Medical Dictionary for Regulatory Activities
13(2)
1.4.2 Common Terminology Criteria for Adverse Events
15(1)
1.4.3 WHO's Adverse Reaction Terminology
16(1)
1.4.4 ICD and COSTART
17(1)
1.5 Serious adverse events and safety signals
18(1)
1.6 Statistical strategies for safety evaluation and a road map for readers
19(2)
1.6.1 Safety data collection and analysis
19(1)
1.6.2 Safety databases and sequential surveillance in pharmacovigilance
20(1)
1.6.3 An interdisciplinary approach and how the book can be used
20(1)
1.7 Supplements and problems
21(4)
2 Biological Models and Associated Statistical Methods
25(44)
2.1 Quantitative structure-activity relationship
27(15)
2.1.1 Toxicity endpoints
27(1)
2.1.2 Molecular descriptors
28(2)
2.1.3 Statistical methods
30(10)
2.1.4 Model validation
40(2)
2.2 Pharmacokinetic-pharmacodynamic models
42(2)
2.3 Analysis of preclinical safety data
44(4)
2.3.1 Carcinogenicity
44(2)
2.3.2 Reproductive and developmental toxicity
46(2)
2.4 Predictive cardiotoxicity
48(6)
2.4.1 Comprehensive in vitro Proarrythmia Assay (CiPA)
49(2)
2.4.2 Phase I ECG studies
51(1)
2.4.3 Concentration-QTc (C-QTc) modeling
52(2)
2.5 Toxicogenomics in predictive toxicology
54(3)
2.5.1 TGx science and technology
54(1)
2.5.2 TGx biomarkers
55(2)
2.6 Regulatory framework in predictive toxicology
57(2)
2.6.1 Regulatory guidelines
57(1)
2.6.2 Safety biomarker qualification
58(1)
2.6.3 In silico models in predictive toxicology
58(1)
2.7 Supplements and problems
59(10)
3 Benefit-Risk Assessment of Medical Products
69(26)
3.1 Some examples of B-R assessment
70(2)
3.1.1 Tysabri
70(1)
3.1.2 Lorcaserin
71(1)
3.1.3 Crizotinib
71(1)
3.2 Ingredients for B-R evaluation
72(2)
3.2.1 Planning process
72(1)
3.2.2 Qualitative and quantitative evaluations
72(1)
3.2.3 Benefit-risk formulations
73(1)
3.3 B-R methods using clinical trials data
74(2)
3.4 Multi-criteria statistical decision theory
76(4)
3.4.1 Multi-criteria decision analysis
76(2)
3.4.2 Stochastic multi-criteria acceptability analysis and statistical decision theory
78(2)
3.5 Quality-adjusted benefit-risk assessments
80(6)
3.5.1 Q-TWiST
80(1)
3.5.2 Quality-adjusted survival analysis
81(3)
3.5.3 Testing QAL differences of treatment from control
84(2)
3.6 Additional statistical methods
86(3)
3.6.1 Number needed to treat (NNT)
86(1)
3.6.2 Incremental net benefits
86(1)
3.6.3 Uncertainty adjustments and Bayesian methods
87(1)
3.6.4 Endpoint selection and other considerations
88(1)
3.7 Supplements and problems
89(6)
4 Design and Analysis of Clinical Trials with Safety End-points
95(46)
4.1 Dose escalation in phase I clinical trials
95(11)
4.1.1 Rule-based designs for cytotoxic treatments
97(1)
4.1.2 CRM, EWOC and other model-based designs
98(4)
4.1.3 Individual versus collective ethics and approximate dynamic programming
102(2)
4.1.4 Extensions to combination therapies
104(1)
4.1.5 Modifications for cytostatic cancer therapies
105(1)
4.2 Safety considerations for the design of phase II and III studies
106(3)
4.2.1 Conditioning on rare adverse events
107(1)
4.2.2 Sequential conditioning and an efficient sequential GLRtest
108(1)
4.3 Phase I--II designs for both efficacy and safety endpoints in cytotoxic cancer treatments
109(2)
4.4 Summary of clinical trial safety data
111(9)
4.4.1 Clinical adverse events
111(1)
4.4.2 Laboratory test results
112(4)
4.4.3 Vital signs
116(1)
4.4.4 Integrated summary of safety (ISS)
117(1)
4.4.5 Development Safety Update Report (DSUR)
118(2)
4.5 EAIR and regression models
120(7)
4.5.1 EAIR and confidence intervals for hazard rates
120(2)
4.5.2 Poisson regression and negative binomial models
122(2)
4.5.3 Rare events data analysis and statistical models for recurrent events
124(3)
4.6 Graphical displays of safety data
127(9)
4.6.1 Graphical displays of proportions and counts
127(3)
4.6.2 Mosaic plots comparing AE severity of treatments
130(1)
4.6.3 Graphical displays for continuous data
131(5)
4.7 Supplements and problems
136(5)
5 Multiplicity in the Evaluation of Clinical Safety Data
141(22)
5.1 An illustrative example
142(4)
5.1.1 A three-tier adverse event categorization system
142(2)
5.1.2 The MMRV combination vaccine trial
144(2)
5.2 Multiplicity issues in efficacy and safety evaluations
146(1)
5.3 P-values, FDR, and some variants
147(4)
5.3.1 Double false discovery rate and its control
148(2)
5.3.2 FDR control for discrete data
150(1)
5.4 Bayesian methods for safety evaluation
151(6)
5.4.1 Berry and Berry's hierarchical mixture model
151(2)
5.4.2 Gould's Bayesian screening model
153(3)
5.4.3 Compound statistical decisions and an empirical Bayes approach
156(1)
5.5 Supplements and Problems
157(6)
6 Causal Inference from Post-Marketing Data
163(42)
6.1 Post-marketing data collection
163(4)
6.1.1 Clinical trials with safety endpoints
164(1)
6.1.2 Observational pharmacoepidemiologic studies using registries
165(1)
6.1.3 Prospective cohort observational studies
166(1)
6.1.4 Retrospective observational studies
166(1)
6.2 Potential outcomes and counterfactuals
167(6)
6.2.1 Causes of effects in attributions for serious adverse health outcomes
167(1)
6.2.2 Counterfactuals, potential outcomes, and Rubin's causal model
168(2)
6.2.3 Frequentist, Bayesian, and missing data approaches
170(3)
6.3 Causal inference from observational studies
173(13)
6.3.1 Matching, subclassification, and standardization
173(2)
6.3.2 Propensity score: Theory and implementation
175(1)
6.3.3 Control for confounding via estimated PS
176(4)
6.3.4 Inverse probability weighting
180(5)
6.3.5 Structural model for latent failure time
185(1)
6.4 Unmeasured confounding
186(5)
6.4.1 Instrumental variables
186(1)
6.4.2 Trend-in-trend research design of observational studies
187(4)
6.5 Structural causal models and causal calculus
191(8)
6.5.1 From structural equation models to SCMs
191(3)
6.5.2 Symbolic causal calculus
194(5)
6.6 Supplements and problems
199(6)
7 Safety Databases: Statistical Analysis and Pharmacovigi-lance
205(56)
7.1 Safety databases
205(8)
7.1.1 Preclinical data
205(4)
7.1.2 Clinical trial data
209(1)
7.1.3 FDA Adverse Event Reporting System (FAERS)
209(1)
7.1.4 Vaccine Adverse Event Reporting System and Vaccine Safety Datalink
210(2)
7.1.5 VigiBase
212(1)
7.1.6 Medicare, Medicaid, and health insurance claims databases
212(1)
7.1.7 Adverse event reporting database for medical devices
213(1)
7.2 Statistical issues in analysis of spontaneous AE databases
213(3)
7.3 Reporting ratios and disproportionality analysis
216(1)
7.4 Empirical Bayes approach to safety signal detection
217(4)
7.5 Bayesian signal detection from AE databases
221(2)
7.6 LR test-based approach and other methods
223(7)
7.6.1 LR test-based approach to QSD
223(2)
7.6.2 Tree-based scan statistics
225(4)
7.6.3 Ontological reasoning approach
229(1)
7.6.4 Deep learning for pharmacovigilance
229(1)
7.7 Meta-analysis of multiple safety studies
230(11)
7.7.1 Fixed and random effects models for meta-analysis
232(5)
7.7.2 Meta-analysis of rare events
237(2)
7.7.3 Network meta-analysis
239(2)
7.8 Pharmacoepidemiologic approaches
241(8)
7.8.1 Information content differences among safety databases and from web-based epidemiologic studies
241(1)
7.8.2 Case-control and self-controlled case series (SCCS)
242(2)
7.8.3 OMOP and systematic pharmacovigilance
244(1)
7.8.4 Postmarketing pharmacoepidemiologic studies: Examples from biologic therapies
245(4)
7.9 Pre-and Post-marketing studies of MMRV vaccine
249(5)
7.9.1 Pre-licensure clinical trials
249(2)
7.9.2 Post-licensure observational safety studies and reversal of ACIP recommendation
251(3)
7.10 Supplements and Problems
254(7)
8 Sequential Methods for Safety Surveillance
261(26)
8.1 Sequential testing for safety surveillance
262(6)
8.1.1 SPRT and CMaxSPRT
262(4)
8.1.2 Adjustments for confounding and risk factors
266(2)
8.2 Group sequential methods
268(4)
8.2.1 Continuous versus group sequential monitoring for post-market safety surveillance
268(1)
8.2.2 Frequency of analyses in sequential surveillance
269(1)
8.2.3 Selection of comparison group and other design considerations
270(2)
8.3 Adjustments in sequential safety surveillance
272(6)
8.3.1 Stratification
272(1)
8.3.2 Matching and applications to VSD and Sentinel data
273(2)
8.3.3 Propensity scores and inverse probability weighting
275(2)
8.3.4 Signal diagnosis
277(1)
8.3.5 Sequential likelihood ratio trend-in-trend design in the presence of unmeasured confounding
278(1)
8.4 Supplements and problems
278(9)
Bibliography 287(62)
Index 349
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.
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