Medical Product Safety Evaluation

Biological Models and Statistical Methods
 
 
Chapman and Hall (Verlag)
  • erschienen am 3. September 2018
  • |
  • 372 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-351-02197-5 (ISBN)
 

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.

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.

  • Englisch
  • London
  • |
  • Großbritannien
Taylor & Francis Ltd
  • Für höhere Schule und Studium
50 schwarz-weiße Abbildungen
978-1-351-02197-5 (9781351021975)

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.

<strong>List of Figures</strong>


<strong></strong>


<strong>List of Tables</strong>


<strong></strong>


<strong>Preface</strong>



<strong>1. Introduction</strong>


Expecting the unexpected


A brief history of medical product regulation


Science of safety


Differences and similarities between efficacy and safety endpoints


Regulatory guidelines and drug withdrawals


Medical product safety, adverse events and adverse drug reactions


Medical product safety


Adverse events versus adverse drug reactions


Safety data coding


Drug dictionaries


WHO Drug Dictionary


Anatomical Therapeutic Chemical (ATC) classification


NCI Drug Dictionary


Adverse event dictionaries


Medical Dictionary for Regulatory Activities (Med-DRA)


Common Terminology Criteria for Adverse Events (CTCAE)


WHO's Adverse Reaction Terminology (WHO-ART)


ICD and COSTART


Serious adverse events and safety signals


Statistical strategies for safety evaluation and a road map for readers


Safety data collection and analysis


Safety databases and sequential surveillance in pharmacovigilance


An interdisciplinary approach and how the book can be read


Supplements and problems

<b>
</b>


<b>2. Biological Models and Associated Statistical Methods </b>


<b>Quantitative structure-activity relationship (QSAR) </b>


<b>Toxicity endpoints </b>


<b>Molecular descriptors </b>


<b>Statistical models in QSAR/QSTR </b>


<b>Model validation </b>


<b>Pharmacokinetic-pharmacodynamic models </b>


<b>Analysis of preclinical safety data </b>


<b>Carcinogenicity </b>


<b>Reproductive and developmental toxicity </b>


<b>Correlated binary and trinary outcomes within litters </b>


<b>Dose response </b>


<b>Predictive cardiotoxicity </b>


<b>The Comprehensive in vitro Proarrythmia Assay (CiPA) </b>


<b>Background </b>


<b>Ion channels, in silico models and stem-cell</b>


<b>derived cardiomyocyte assays </b>


<b>Phase I ECG studies </b>


<b>Concentration-QTc modeling </b>


<b>Toxicogenomics in predictive toxicology </b>


<b>Components of TGx </b>


<b>TGx biomarkers </b>


<b>Regulatory framework in predictive toxicology </b>


<b>Regulatory guidelines </b>


<b>Safety biomarker qualification </b>


<b>In silico models in predictive toxicology </b>


<b>Supplements and problems </b>

<b><b>
</b></b>


<b><b>3. Benefit-Risk </b></b>


<b><b>Some examples of B-R assessment </b></b>


<b><b>Tysabri </b></b>


<b><b>Lorcaserin </b></b>


<b><b>Crizotinib </b></b>


<b><b>Critical ingredients for B-R evaluation </b></b>


<b><b>Planning process </b></b>


<b><b>Qualitative and quantitative evaluations </b></b>


<b><b>Benefit-risk formulations </b></b>


<b><b>A multidisciplinary approach incorporating multiple perspectives </b></b>


<b><b>Multi-criteria statistical decision theory </b></b>


<b><b>Multi-criteria decision analysis </b></b>


<b><b>Stochastic multi-criteria acceptability analysis </b></b>


<b><b>Stochastic multi-criteria discriminatory method </b></b>


<b><b>B-R methods using clinical trial data </b></b>


<b><b>Quality-adjusted benefit-risk assessment methods </b></b>


<b><b>Q-TWiST </b></b>


<b><b>Quality-adjusted survival analysis </b></b>


<b><b>Testing QAL differences between treatment and control</b></b>


<b><b>Additional statistical methods </b></b>


<b><b>Number needed to treat(NNT) </b></b>


<b><b>Incremental net benefits (INB) </b></b>


<b><b>Weighting schemes, uncertainty, models, supplemental data and patient-level data </b></b>


<b><b>Bayesian methods </b></b>


<b><b>Endpoint selection and other considerations </b></b>


<b><b>Other statistical considerations </b></b>


<b><b>Supplements and problems </b></b>

<b><b><b>
</b></b></b>


<b><b><b>4. Design and Analysis of Clinical Trials with Safety Endpoints</b></b></b>


<b><b><b>Dose escalation in phase I clinical trials </b></b></b>


<b><b><b>Rule-based designs </b></b></b>


<b><b><b>Model-based designs: CRM EWOC, Bayesian threshold designs </b></b></b>


<b><b><b>Individual versus collective ethics and approximate dynamic programming </b></b></b>


<b><b><b>Extensions to combination therapies </b></b></b>


<b><b><b>Modifications for cytostatic cancer therapies </b></b></b>


<b><b><b>Safety considerations for the design of phase II and III studies</b></b></b>


<b><b><b>Challenges of safety evaluation in phase II andphase III trials </b></b></b>


<b><b><b>Conditioning on rare adverse events and the RESTexample </b></b></b>


<b><b><b>A sequential conditioning method and an efficient sequential GLR test </b></b></b>


<b><b><b>Designs for both efficacy and safety endpoints </b></b></b>


<b><b><b>Summary of clinical trial safety data </b></b></b>


<b><b><b>Integrated summary of safety (ISS) </b></b></b>


<b><b><b>Development safety update </b></b></b>


<b><b><b>Clinical safety endpoints </b></b></b>


<b><b><b>Laboratory test results </b></b></b>


<b><b><b>Vital signs </b></b></b>


<b><b><b>Contents</b></b></b>


<b><b><b>Graphic display of safety data </b></b></b>


<b><b><b>Graphic display for proportions and counts </b></b></b>


<b><b><b>Graphic displays for continuous data </b></b></b>


<b><b><b>Statistical methods for the analysis of clinical safety data </b></b></b>


<b><b><b>Incidence rates and confidence intervals </b></b></b>


<b><b><b>Confidence intervals based on Wald's approximation and moment </b></b></b>


<b><b><b>Confidence intervals based on variance estimate recovery </b></b></b>


<b><b><b>Confidence intervals based on parameter constraint </b></b></b>


<b><b><b>Confidence intervals with stratification </b></b></b>


<b><b><b>Regression models </b></b></b>


<b><b><b>Poisson regression </b></b></b>


<b><b><b>Negative binomial models </b></b></b>


<b><b><b>Rare event analysis </b></b></b>


<b><b><b>Zero-inflated regression models </b></b></b>


<b><b><b>Generalized extreme value regression </b></b></b>


<b><b><b>Time-to-event analysis and competing risks </b></b></b>


<b><b><b>Recurrent events </b></b></b>


<b><b><b>Mean cumulative function </b></b></b>


<b><b><b>Regression models </b></b></b>


<b><b><b>Supplements and problems </b></b></b>

<b><b><b><b>
</b></b></b></b>


<b><b><b><b>5. Multiplicity in the Evaluation of Clinical Safety Data </b></b></b></b>


<b><b><b><b>An illustrative example </b></b></b></b>


<b><b><b><b>A three-tier adverse event categorization system </b></b></b></b>


<b><b><b><b>The MMRV combination vaccine trial </b></b></b></b>


<b><b><b><b>Multiplicity issues in efficacy and safety evaluations </b></b></b></b>


<b><b><b><b>P-values, FDR and some variants </b></b></b></b>


<b><b><b><b>Double false discovery rate and its control </b></b></b></b>


<b><b><b><b>FDR control for discrete data </b></b></b></b>


<b><b><b><b>Bayesian methods for safety evaluation </b></b></b></b>


<b><b><b><b>Berry and Berry's hierarchical mixture model </b></b></b></b>


<b><b><b><b>Gould's Bayesian screening model </b></b></b></b>


<b><b><b><b>Compound decisions and an empirical Bayes approach</b></b></b></b>


<b><b><b><b>Supplements and Problems </b></b></b></b>

<b><b><b><b><b>
</b></b></b></b></b>


<b><b><b><b><b>6. Causal Inference from Post-Marketing Data </b></b></b></b></b>


<b><b><b><b><b>Post-marketing data collection </b></b></b></b></b>


<b><b><b><b><b>Clinical trials with safety endpoints </b></b></b></b></b>


<b><b><b><b><b>Observational pharmacoepidemiologic studies using registries </b></b></b></b></b>


<b><b><b><b><b>Prospective cohort observational studies </b></b></b></b></b>


<b><b><b><b><b>Retrospective observational studies </b></b></b></b></b>


<b><b><b><b><b>Potential outcomes and counterfactuals </b></b></b></b></b>


<b><b><b><b><b>Causes of effects in attributions for serious adverse health outcomes </b></b></b></b></b>


<b><b><b><b><b>Counterfactuals, potential outcomes, and Rubin's causal model </b></b></b></b></b>


<b><b><b><b><b>Frequentist, Bayesian, and missing data approaches </b></b></b></b></b>


<b><b><b><b><b>Causal inference from observational studies </b></b></b></b></b>


<b><b><b><b><b>Matching, sub classification, and standardization </b></b></b></b></b>


<b><b><b><b><b>Propensity score: Theory and implementation </b></b></b></b></b>


<b><b><b><b><b>Control for confounding via estimated propensityscore </b></b></b></b></b>


<b><b><b><b><b>Inverse probability weighting </b></b></b></b></b>


<b><b><b><b><b>Unmeasured confounding </b></b></b></b></b>


<b><b><b><b><b>Instrumental variables </b></b></b></b></b>


<b><b><b><b><b>Econometrics background, instrumental variable tests and GMM extensions </b></b></b></b></b>


<b><b><b><b><b>Trend-in-trend research design of observational studies </b></b></b></b></b>


<b><b><b><b><b>Supplements and problems </b></b></b></b></b>

<b><b><b><b><b><b>
</b></b></b></b></b></b>


<b><b><b><b><b><b>7. Safety Databases: Statistical Analysis and Pharmacovigilance</b></b></b></b></b></b>


<b><b><b><b><b><b>Safety databases </b></b></b></b></b></b>


<b><b><b><b><b><b>Preclinical data </b></b></b></b></b></b>


<b><b><b><b><b><b>Clinical trial data </b></b></b></b></b></b>


<b><b><b><b><b><b>FDA Adverse Event Reporting System (FAERS) </b></b></b></b></b></b>


<b><b><b><b><b><b>Vaccine Adverse Event Reporting System and Vaccine</b></b></b></b></b></b>


<b><b><b><b><b><b>Safety Link </b></b></b></b></b></b>


<b><b><b><b><b><b>VigiBase </b></b></b></b></b></b>


<b><b><b><b><b><b>Medicare, Medicaid Database, and health insurance claims databases </b></b></b></b></b></b>


<b><b><b><b><b><b>Adverse event reporting database for medical devices</b></b></b></b></b></b>


<b><b><b><b><b><b>Statistical issues in analysis of spontaneous AE databases </b></b></b></b></b></b>


<b><b><b><b><b><b>Statistical methods for the analysis of safety database </b></b></b></b></b></b>


<b><b><b><b><b><b>Reporting ratios and disproportionality analysis </b></b></b></b></b></b>


<b><b><b><b><b><b>Empirical Bayes shrinkage estimation of log ratios </b></b></b></b></b></b>


<b><b><b><b><b><b>Combining results from multiple safety studies by meta-analysis </b></b></b></b></b></b>


<b><b><b><b><b><b>Fixed effects model for combining studies </b></b></b></b></b></b>


<b><b><b><b><b><b>Random effect model for combining studies </b></b></b></b></b></b>


<b><b><b><b><b><b>Meta-analysis of rare events </b></b></b></b></b></b>


<b><b><b><b><b><b>Meta-analysis using individual subject data </b></b></b></b></b></b>


<b><b><b><b><b><b>Bayesian meta-analysis </b></b></b></b></b></b>


<b><b><b><b><b><b>Pharmacoepidemiologic approaches </b></b></b></b></b></b>


<b><b><b><b><b><b>Information content differences among different safety databases and from web-based epidemiologic studies </b></b></b></b></b></b>


<b><b><b><b><b><b>Case-control and self-controlled case series (SCCS) approaches </b></b></b></b></b></b>


<b><b><b><b><b><b>OMOP and systematic pharmacovigilance </b></b></b></b></b></b>


<b><b><b><b><b><b>Postmarketing pharmacoepidemiologic studies: Examples from biologic therapies </b></b></b></b></b></b>


<b><b><b><b><b><b>Quantitative signal detection and machine learning </b></b></b></b></b></b>


<b><b><b><b><b><b>Likelihood ratio tests </b></b></b></b></b></b>


<b><b><b><b><b><b>Supplements and problems </b></b></b></b></b></b>

<b><b><b><b><b><b><b>
</b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>8. Sequential Methods for Safety Surveillance </b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>Overview of sequential change detection and diagnosis </b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>Combining sequential testing and detection for pharmacovigilance</b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>MaxSPRT, CMaxSPRT and sequential GLR tests </b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>Implementation of CMaxSPRT </b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>Adjustment for confounding </b></b></b></b></b></b></b>


<b><b><b><b><b><b><b>Supplement and problems </b></b></b></b></b></b></b>

<b><b><b><b><b><b><b><b>
</b></b></b></b></b></b></b></b>


<b><b><b><b><b><b><b><b>Bibliography </b></b></b></b></b></b></b></b>



<b><b><b><b><b><b><b><b>Index </b></b></b></b></b></b></b></b>

"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."
<em>~Ivan Chan, AbbVie </em>

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