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.

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.

List of Figures

List of Tables

Preface

1. Introduction

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

Quantitative structure-activity relationship (QSAR)

Toxicity endpoints

Molecular descriptors

Statistical models in QSAR/QSTR

Model validation

Pharmacokinetic-pharmacodynamic models

Analysis of preclinical safety data

Carcinogenicity

Reproductive and developmental toxicity

Correlated binary and trinary outcomes within litters

Dose response

Predictive cardiotoxicity

The Comprehensive in vitro Proarrythmia Assay (CiPA)

Background

Ion channels, in silico models and stem-cell

derived cardiomyocyte assays

Phase I ECG studies

Concentration-QTc modeling

Toxicogenomics in predictive toxicology

Components of TGx

TGx biomarkers

Regulatory framework in predictive toxicology

Regulatory guidelines

Safety biomarker qualification

In silico models in predictive toxicology

Supplements and problems

Some examples of B-R assessment

Tysabri

Lorcaserin

Crizotinib

Critical ingredients for B-R evaluation

Planning process

Qualitative and quantitative evaluations

Benefit-risk formulations

A multidisciplinary approach incorporating multiple perspectives

Multi-criteria statistical decision theory

Multi-criteria decision analysis

Stochastic multi-criteria acceptability analysis

Stochastic multi-criteria discriminatory method

B-R methods using clinical trial data

Quality-adjusted benefit-risk assessment methods

Q-TWiST

Quality-adjusted survival analysis

Testing QAL differences between treatment and control

Additional statistical methods

Number needed to treat(NNT)

Incremental net benefits (INB)

Weighting schemes, uncertainty, models, supplemental data and patient-level data

Bayesian methods

Endpoint selection and other considerations

Other statistical considerations

Supplements and problems

Dose escalation in phase I clinical trials

Rule-based designs

Model-based designs: CRM EWOC, Bayesian threshold designs

Individual versus collective ethics and approximate dynamic programming

Extensions to combination therapies

Modifications for cytostatic cancer therapies

Safety considerations for the design of phase II and III studies

Challenges of safety evaluation in phase II andphase III trials

Conditioning on rare adverse events and the RESTexample

A sequential conditioning method and an efficient sequential GLR test

Designs for both efficacy and safety endpoints

Summary of clinical trial safety data

Integrated summary of safety (ISS)

Development safety update

Clinical safety endpoints

Laboratory test results

Vital signs

Contents

Graphic display of safety data

Graphic display for proportions and counts

Graphic displays for continuous data

Statistical methods for the analysis of clinical safety data

Incidence rates and confidence intervals

Confidence intervals based on Wald's approximation and moment

Confidence intervals based on variance estimate recovery

Confidence intervals based on parameter constraint

Confidence intervals with stratification

Regression models

Poisson regression

Negative binomial models

Rare event analysis