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Statistical Methods for Drug Safety

Statistical Methods for Drug Safety Author Robert D. Gibbons
ISBN-10 9781466561854
Release 2015-07-28
Pages 308
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Explore Important Tools for High-Quality Work in Pharmaceutical Safety Statistical Methods for Drug Safety presents a wide variety of statistical approaches for analyzing pharmacoepidemiologic data. It covers both commonly used techniques, such as proportional reporting ratios for the analysis of spontaneous adverse event reports, and newer approaches, such as the use of marginal structural models for controlling dynamic selection bias in the analysis of large-scale longitudinal observational data. Choose the Right Statistical Approach for Analyzing Your Drug Safety Data The book describes linear and non-linear mixed-effects models, discrete-time survival models, and new approaches to the meta-analysis of rare binary adverse events. It explores research involving the re-analysis of complete longitudinal patient records from randomized clinical trials. The book discusses causal inference models, including propensity score matching, marginal structural models, and differential effects, as well as mixed-effects Poisson regression models for analyzing ecological data, such as county-level adverse event rates. The authors also cover numerous other methods useful for the analysis of within-subject and between-subject variation in adverse events abstracted from large-scale medical claims databases, electronic health records, and additional observational data streams. Advance Statistical Practice in Pharmacoepidemiology Authored by two professors at the forefront of developing new statistical methodologies to address pharmacoepidemiologic problems, this book provides a cohesive compendium of statistical methods that pharmacoepidemiologists can readily use in their work. It also encourages statistical scientists to develop new methods that go beyond the foundation covered in the text.

Statistical Design and Analysis of Stability Studies

Statistical Design and Analysis of Stability Studies Author Shein-Chung Chow
ISBN-10 1584889063
Release 2007-05-30
Pages 352
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The US Food and Drug Administration's Report to the Nation in 2004 and 2005 indicated that one of the top reasons for drug recall was that stability data did not support existing expiration dates. Pharmaceutical companies conduct stability studies to characterize the degradation of drug products and to estimate drug shelf life. Illustrating how stability studies play an important role in drug safety and quality assurance, Statistical Design and Analysis of Stability Studies presents the principles and methodologies in the design and analysis of stability studies. After introducing the basic concepts of stability testing, the book focuses on short-term stability studies and reviews several methods for estimating drug expiration dating periods. It then compares some commonly employed study designs and discusses both fixed and random batch statistical analyses. Following a chapter on the statistical methods for stability analysis under a linear mixed effects model, the book examines stability analyses with discrete responses, multiple components, and frozen drug products. In addition, the author provides statistical methods for dissolution testing and explores current issues and recent developments in stability studies. To ensure the safety of consumers, professionals in the field must carry out stability studies to determine the reliability of drug products during their expiration period. This book provides the material necessary for you to perform stability designs and analyses in pharmaceutical research and development.

Statistical Methods for Immunogenicity Assessment

Statistical Methods for Immunogenicity Assessment Author Harry Yang
ISBN-10 9781498700351
Release 2015-07-17
Pages 259
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Develop Effective Immunogenicity Risk Mitigation Strategies Immunogenicity assessment is a prerequisite for the successful development of biopharmaceuticals, including safety and efficacy evaluation. Using advanced statistical methods in the study design and analysis stages is therefore essential to immunogenicity risk assessment and mitigation strategies. Statistical Methods for Immunogenicity Assessment provides a single source of information on statistical concepts, principles, methods, and strategies for detection, quantification, assessment, and control of immunogenicity. The book first gives an overview of the impact of immunogenicity on biopharmaceutical development, regulatory requirements, and statistical methods and strategies used for immunogenicity detection, quantification, and risk assessment and mitigation. It then covers anti-drug antibody (ADA) assay development, optimization, validation, and transfer as well as the analysis of cut point, a key assay performance parameter in ADA assay development and validation. The authors illustrate how to apply statistical modeling approaches to establish associations between ADA and clinical outcomes, predict immunogenicity risk, and develop risk mitigation strategies. They also present various strategies for immunogenicity risk control. The book concludes with an explanation of the computer codes and algorithms of the statistical methods. A critical issue in the development of biologics, immunogenicity can cause early termination or limited use of the products if not managed well. This book shows how to use robust statistical methods for detecting, quantifying, assessing, and mitigating immunogenicity risk. It is an invaluable resource for anyone involved in immunogenicity risk assessment and control in both non-clinical and clinical biopharmaceutical development.

Data and Safety Monitoring Committees in Clinical Trials Second Edition

Data and Safety Monitoring Committees in Clinical Trials  Second Edition Author Jay Herson
ISBN-10 9781351722377
Release 2016-12-19
Pages 241
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Praise for the first edition: "Given the author’s years of experience as a statistician and as a founder of the first DMC in pharmaceutical industry trials, I highly recommend this book—not only for experts because of its cogent and organized presentation, but more importantly for young investigators who are seeking information about the logistical and philosophical aspects of a DMC." -S. T. Ounpraseuth, The American Statistician ? In the first edition of this well-regarded book, the author provided a groundbreaking and definitive guide to best practices in pharmaceutical industry data monitoring committees (DMCs). Maintaining all the material from the first edition and adding substantial new material, Data and Safety Monitoring Committees in Clinical Trials, Second Edition is ideal for training professionals to serve on their first DMC as well as for experienced clinical and biostatistical DMC members, sponsor and regulatory agency staff. The second edition guides the reader through newly emerging DMC responsibilities brought about by regulations emphasizing risk vs benefit and the emergence of risk-based monitoring. It also provides the reader with many new statistical methods, clinical trial designs and clinical terminology that have emerged since the first edition. The references have been updated and the very popular end-of-chapter Q&A section has been supplemented with many new experiences since the first edition. ? New to the Second Edition: Presents statistical methods, tables, listings and graphs appropriate for safety review, efficacy analysis and risk vs benefit analysis, SPERT and PRISMA initiatives. Newly added interim analysis for efficacy and futility section. DMC responsibilities in SUSARs (Serious Unexpected Serious Adverse Reactions), basket trials, umbrella trials, dynamic treatment strategies /SMART trials, pragmatic trials, biosimilar trials, companion diagnostics, etc. DMC responsibilities for data quality and fraud detection (Fraud Recovery Plan) Use of patient reported outcomes of safety Use of meta analysis and data outside the trial New ideas for training and compensation of DMC members ? Jay Herson is Senior Associate, Biostatistics, Johns Hopkins Bloomberg School of Public Health where he teaches courses on clinical trials and drug development based on his many years experience in clinical trials in academia and the pharmaceutical industry. ? ? ? ? ? ? ? ? ? ? ? ? ?

Statistical Design and Analysis of Clinical Trials

Statistical Design and Analysis of Clinical Trials Author Weichung Joe Shih
ISBN-10 9781482250503
Release 2015-07-28
Pages 244
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Statistical Design and Analysis of Clinical Trials: Principles and Methods concentrates on the biostatistics component of clinical trials. Developed from the authors’ courses taught to public health and medical students, residents, and fellows during the past 15 years, the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. Teach Your Students How to Design, Monitor, and Analyze Clinical Trials The book begins with ethical and safety principles, core trial design concepts, the principles and methods of sample size and power calculation, and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials, covering monitoring safety, futility, and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures, explain the concept of different missing data processes, and describe how to analyze incomplete data by proper multiple imputations. Turn Your Students into Better Clinical Trial Investigators This text reflects the academic research, commercial development, and public health aspects of clinical trials. It gives students a multidisciplinary understanding of the concepts and techniques involved in designing and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students in (bio)statistics, epidemiology, medicine, pharmacy, and public health.

Multiple Testing Problems in Pharmaceutical Statistics

Multiple Testing Problems in Pharmaceutical Statistics Author Alex Dmitrienko
ISBN-10 1584889853
Release 2009-12-08
Pages 320
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Useful Statistical Approaches for Addressing Multiplicity Issues Includes practical examples from recent trials Bringing together leading statisticians, scientists, and clinicians from the pharmaceutical industry, academia, and regulatory agencies, Multiple Testing Problems in Pharmaceutical Statistics explores the rapidly growing area of multiple comparison research with an emphasis on pharmaceutical applications. In each chapter, the expert contributors describe important multiplicity problems encountered in pre-clinical and clinical trial settings. The book begins with a broad introduction from a regulatory perspective to different types of multiplicity problems that commonly arise in confirmatory controlled clinical trials, before giving an overview of the concepts, principles, and procedures of multiple testing. It then presents statistical methods for analyzing clinical dose response studies that compare several dose levels with a control as well as statistical methods for analyzing multiple endpoints in clinical trials. After covering gatekeeping procedures for testing hierarchically ordered hypotheses, the book discusses statistical approaches for the design and analysis of adaptive designs and related confirmatory hypothesis testing problems. The final chapter focuses on the design of pharmacogenomic studies based on established statistical principles. It also describes the analysis of data collected in these studies, taking into account the numerous multiplicity issues that occur. This volume explains how to solve critical issues in multiple testing encountered in pre-clinical and clinical trial applications. It presents the necessary statistical methodology, along with examples and software code to show how to use the methods in practice.

Bioequivalence and Statistics in Clinical Pharmacology Second Edition

Bioequivalence and Statistics in Clinical Pharmacology  Second Edition Author Scott D. Patterson
ISBN-10 9781466585218
Release 2017-03-27
Pages 434
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Maintaining a practical perspective, Bioequivalence and Statistics in Clinical Pharmacology, Second Edition explores statistics used in day-to-day clinical pharmacology work. The book is a starting point for those involved in such research and covers the methods needed to design, analyze, and interpret bioequivalence trials; explores when, how, and why these studies are performed as part of drug development; and demonstrates the methods using real world examples. Drawing on knowledge gained directly from working in the pharmaceutical industry, the authors set the stage by describing the general role of statistics. Once the foundation of clinical pharmacology drug development, regulatory applications, and the design and analysis of bioequivalence trials are established,?including recent regulatory changes in design and analysis and in particular sample-size adaptation, they move on to related topics in clinical pharmacology involving the use of cross-over designs. These include, but are not limited to, safety studies in Phase I, dose-response trials, drug interaction trials, food-effect and combination trials, QTc and other pharmacodynamic equivalence trials, proof-of-concept trials, dose-proportionality trials, and vaccines trials.? This second edition addresses several recent developments in the field, including new chapters on adaptive bioequivalence studies, scaled average bioequivalence testing, and vaccine trials. Purposefully designed to be instantly applicable, Bioequivalence and Statistics in Clinical Pharmacology, Second Edition provides examples of SAS and R code so that the analyses described can be immediately implemented. The authors have made extensive use of the proc mixed procedures available in SAS.

Design and Analysis of Non Inferiority Trials

Design and Analysis of Non Inferiority Trials Author Mark D. Rothmann
ISBN-10 9781584888055
Release 2016-04-19
Pages 454
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The increased use of non-inferiority analysis has been accompanied by a proliferation of research on the design and analysis of non-inferiority studies. Using examples from real clinical trials, Design and Analysis of Non-Inferiority Trials brings together this body of research and confronts the issues involved in the design of a non-inferiority trial. Each chapter begins with a non-technical introduction, making the text easily understood by those without prior knowledge of this type of trial. Topics covered include: A variety of issues of non-inferiority trials, including multiple comparisons, missing data, analysis population, the use of safety margins, the internal consistency of non-inferiority inference, the use of surrogate endpoints, trial monitoring, and equivalence trials Specific issues and analysis methods when the data are binary, continuous, and time-to-event The history of non-inferiority trials and the design and conduct considerations for a non-inferiority trial The strength of evidence of an efficacy finding and how to evaluate the effect size of an active control therapy A comprehensive discussion on the purpose and issues involved with non-inferiority trials, Design and Analysis of Non-inferiority Trials will assist current and future scientists and statisticians on the optimal design of non-inferiority trials and in assessing the quality of non-inferiority comparisons done in practice.

Clinical Trial Data Analysis Using R and SAS

Clinical Trial Data Analysis Using R and SAS Author Ding-Geng (Din) Chen
ISBN-10 9781351651141
Release 2017-06-01
Pages 378
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Review of the First Edition "The goal of this book, as stated by the authors, is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall, this book achieves the goal successfully and does a nice job. I would highly recommend it ...The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods." —Journal of Statistical Software Clinical Trial Data Analysis Using R and SAS, Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book’s practical, detailed approach draws on the authors’ 30 years’ experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS, which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data. What’s New in the Second Edition Adds SAS programs along with the R programs for clinical trial data analysis. Updates all the statistical analysis with updated R packages. Includes correlated data analysis with multivariate analysis of variance. Applies R and SAS to clinical trial data from hypertension, duodenal ulcer, beta blockers, familial andenomatous polyposis, and breast cancer trials. Covers the biostatistical aspects of various clinical trials, including treatment comparisons, time-to-event endpoints, longitudinal clinical trials, and bioequivalence trials.

Quantitative Evaluation of Safety in Drug Development

Quantitative Evaluation of Safety in Drug Development Author Qi Jiang
ISBN-10 9781466555457
Release 2014-12-08
Pages 382
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State-of-the-Art Methods for Drug Safety Assessment Responding to the increased scrutiny of drug safety in recent years, Quantitative Evaluation of Safety in Drug Development: Design, Analysis and Reporting explains design, monitoring, analysis, and reporting issues for both clinical trials and observational studies in biopharmaceutical product development. It presents the latest statistical methods for drug safety assessment. The book’s three sections focus on study design, safety monitoring, and data evaluation/analysis. The book addresses key challenges across regulatory agencies, industry, and academia. It discusses quantitative approaches to safety evaluation and risk management in drug development, covering Bayesian methods, effective safety graphics, and risk-benefit evaluation. Written by a team of experienced leaders, this book brings the most advanced knowledge and statistical methods of drug safety to the statistical, clinical, and safety community. It shares best practices and stimulates further research and methodology development in the drug safety area.

Self Controlled Case Series Studies

Self Controlled Case Series Studies Author Paddy Farrington
ISBN-10 9780429957529
Release 2018-05-03
Pages 362
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"The self-controlled case series has emerged as a key methodology for studying the effects of healthcare interventions. The overall literature around the self-controlled case series has exploded in recent years and this important and timely book pulls it all together in an effective and clear manner. It certainly belongs on the shelf (or beside the keyboard) of every analyst conducting observational studies in healthcare." —David Madigan, Columbia University Self-Controlled Case Series Studies: A Modelling Guide with R provides the first comprehensive account of the self-controlled case series (SCCS) method, a statistical technique for investigating associations between outcome events and time-varying exposures. The method only requires information from individuals who have experienced the event of interest, and automatically controls for multiplicative time-invariant confounders, even when these are unmeasured or unknown. It is increasingly being used in epidemiology, most frequently to study the safety of vaccines and pharmaceutical drugs. Key features of the book include: A thorough yet accessible description of the SCCS method, with mathematical details provided in separate starred sections. Comprehensive discussion of assumptions and how they may be verified. A detailed account of different SCCS models, extensions of the SCCS method, and the design of SCCS studies. Extensive practical illustrations and worked examples from epidemiology. Full computer code from the associated R package SCCS, which includes all the data sets used in the book. The book is aimed at a broad range of readers, including epidemiologists and medical statisticians who wish to use the SCCS method, and also researchers with an interest in statistical methodology. The three authors have been closely involved with the inception, development, popularisation and programming of the SCCS method.

Medical Product Safety Evaluation

Medical Product Safety Evaluation Author Jie Chen
ISBN-10 1466508086
Release 2018
Pages 338
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Medical Product Safety Evaluation: Biological Models and Statistical Methodspresents 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 Chenis 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 Laiis 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.

Statistical Testing Strategies in the Health Sciences

Statistical Testing Strategies in the Health Sciences Author Albert Vexler
ISBN-10 9781315353012
Release 2017-12-21
Pages 703
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Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making, ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments. The book is organized primarily based on the type of questions to be answered by inference procedures or according to the general type of mathematical derivation. It establishes the theoretical framework for each method, with a substantial amount of chapter notes included for additional reference. It then focuses on the practical application for each concept, providing real-world examples that can be easily implemented using corresponding statistical software code in R and SAS. The book also explains the basic elements and methods for constructing correct and powerful statistical decision-making processes to be adapted for complex statistical applications. With techniques spanning robust statistical methods to more computationally intensive approaches, this book shows how to apply correct and efficient testing mechanisms to various problems encountered in medical and epidemiological studies, including clinical trials. Theoretical statisticians, medical researchers, and other practitioners in epidemiology and clinical research will appreciate the book’s novel theoretical and applied results. The book is also suitable for graduate students in biostatistics, epidemiology, health-related sciences, and areas pertaining to formal decision-making mechanisms.

Statistical Methods in Drug Combination Studies

Statistical Methods in Drug Combination Studies Author Wei Zhao
ISBN-10 9781482216752
Release 2014-12-19
Pages 240
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The growing interest in using combination drugs to treat various complex diseases has spawned the development of many novel statistical methodologies. The theoretical development, coupled with advances in statistical computing, makes it possible to apply these emerging statistical methods in in vitro and in vivo drug combination assessments. However, despite these advances, no book has served as a single source of information for statistical methods in drug combination research, nor has there been any guidance for experimental strategies. Statistical Methods in Drug Combination Studies fills that gap, covering all aspects of drug combination research, from designing in vitro drug combination studies to analyzing clinical trial data. Featuring contributions from researchers in industry, academia, and regulatory agencies, this comprehensive reference: Describes statistical models used to characterize dose–response patterns of monotherapies and evaluate the combination drug synergy Offers guidance for estimating interaction indices and constructing their associated confidence intervals to assess drug interaction Introduces a practical and innovative Bayesian approach to Phase I cancer trials, including actual trial examples to illustrate use Examines strategies in the fixed-dose combination therapy clinical development via case studies stemming from regulatory reviews Evaluates computational tools and software packages used to apply novel statistical methods in combination drug development Statistical Methods in Drug Combination Studies provides researchers with a solid understanding of the available statistical methods and computational tools and how to apply them in drug combination studies. The book is equally useful for statisticians to become better equipped to deal with drug combination study design and analysis in their practice.

Multiregional Clinical Trials for Simultaneous Global New Drug Development

Multiregional Clinical Trials for Simultaneous Global New Drug Development Author Joshua Chen
ISBN-10 9781498701488
Release 2016-04-21
Pages 353
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In a global clinical development strategy, multiregional clinical trials (MRCTs) are vital in the development of innovative medicines. Multiregional Clinical Trials for Simultaneous Global New Drug Development presents a comprehensive overview on the current status of conducting MRCTs in clinical development. International experts from academia, industry, and health organizations address various aspects of the important problems in global clinical development and MRCTs. The book first provides a high-level introduction to the context, motivation, opportunities, and challenges in simultaneous global clinical development using MRCTs. It then focuses on the design, monitoring, and analysis/interpretation of MRCTs. The book concludes with an examination of the latest research topics from MRCT perspectives, such as special considerations by local health authorities, health economic evaluations, benefit-risk assessment, and medical devices. Explaining how to design, conduct, and interpret MRCTs, this book will help biostatisticians working in the late-stage clinical development of medical products. It will also be useful for statisticians and clinicians in the biopharmaceutical industry, regulatory agencies, and medical research institutes.

Benefit Risk Assessment in Pharmaceutical Research and Development

Benefit Risk Assessment in Pharmaceutical Research and Development Author Andreas Sashegyi
ISBN-10 9781439867952
Release 2013-11-27
Pages 220
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Many practitioners in the pharmaceutical industry are still largely unfamiliar with benefit-risk assessment, despite its growing prominence in drug development and commercialization. Helping to alleviate this knowledge gap, Benefit-Risk Assessment in Pharmaceutical Research and Development provides a succinct overview of the key considerations relevant to benefit-risk assessment across the pharmaceutical R&D spectrum, from early clinical development to late-stage development to regulatory review to post-launch assessment. The book first presents interpretations of benefit and risk in the context of a molecule moving from preclinical evaluation into its early testing in humans. It next considers benefit and risk characterization and assessment during a molecule’s journey from its clinical evaluation in humans through its submission to regulators for marketing approval. Throughout these sections, the book offers insight into the role of benefit-risk assessment in heightening understanding among key stakeholders by shaping questions and guiding discussions among scientists, physicians, developers, and regulatory agencies. The book also focuses on a molecule’s entry into the marketplace as a drug available for consumption by people. It explores the role of benefit-risk assessment as the relevance of carefully collected clinical efficacy and safety metrics fades in the wake of real-world use and evidence of effectiveness and safety. Bringing together the expertise of 15 contributors from academia and the industry, this book offers an easy-to-read guide to the various facets of benefit-risk assessment in the major stages of pharmaceutical R&D. Suitable for those in both technical and managerial roles, it enables readers to communicate more effectively across their development chain as well as rationally and thoughtfully embed benefit-risk assessment into their R&D processes.

Translational Medicine

Translational Medicine Author Dennis Cosmatos
ISBN-10 1584888733
Release 2008-12-17
Pages 224
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Examines Critical Decisions for Transitioning Lab Science to a Clinical Setting The development of therapeutic pharmaceutical compounds is becoming more expensive, and the success rates for getting such treatments approved for marketing and to the patients is decreasing. As a result, translational medicine (TM) is becoming increasingly important in the healthcare industry – a means of maximizing the consideration and use of information collected as compounds transition from initial lab discovery, through pre-clinical testing, early clinical trials, and late confirmatory studies that lead to regulatory approval of drug release to patients. Translational Medicine: Strategies and Statistical Methods suggests a process for transitioning from the initial lab discovery to the patient’s bedside with minimal disconnect and offers a comprehensive review of statistical design and methodology commonly employed in this bench-to-bedside research. Documents Alternative Research Approaches for Faster and More Accurate Data Judgment Calls Elaborating on how to introduce TM into clinical studies, this authoritative work presents a keen approach to building, executing, and validating statistical models that consider data from various phases of development. It also delineates a truly translational example to help bolster understanding of discussed concepts. This comprehensive guide effectively demonstrates how to overcome obstacles related to successful TM practice. It contains invaluable information for pharmaceutical scientists, research executives, clinicians, and biostatisticians looking to expedite successful implementation of this important process.