Clinical trials Books
Taylor & Francis Ltd Noninferiority Testing in Clinical Trials
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£45.99
Taylor & Francis Ltd Design and Analysis of Bridging Studies
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£43.69
Taylor & Francis Ltd Design and Analysis of NonInferiority Trials
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£43.69
Taylor & Francis Ltd Design and Analysis of Pragmatic Trials
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£87.39
Taylor & Francis Ltd SingleArm Phase II Survival Trial Design
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£142.50
Taylor & Francis Ltd SingleArm Phase II Survival Trial Design
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£43.69
Taylor & Francis Ltd Applied MetaAnalysis with R and Stata
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£45.99
Taylor & Francis Ltd Analytical Similarity Assessment in Biosimilar Product Development
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£47.49
Taylor & Francis Ltd RealWorld Evidence in a PatientCentric Digital
Book SynopsisReal-world evidence is defined as evidence generated from real-world data outside randomized controlled trials. As scientific discoveries and methodologies continue to advance, real-world data and their companion technologies offer powerful new tools for evidence generation. Real-World Evidence in a Patient-Centric Digital Era provides perspectives, examples, and insights on the innovative application of real-world evidence to meet patient needs and improve healthcare, with a focus on the pharmaceutical industry.This book presents an overview of key analytical issues and best practices. Special attention is paid to the development, methodologies, and other salient features of the statistical and data science techniques that are customarily used to generate real-world evidence. It provides a review of key topics and emerging trends in cutting-edge data science and health innovation.Features: Provides an overview Table of ContentsPreface: Real World Evidence and Digital Innovation to Combat Noncommunicable Diseases. 1. Real World Evidence Generation. 2. Applications of RWE for Regulatory Uses. 3. Ethics & Bioethics. 4. Real- World Data, Big Data and Artificial Intelligence: Recent Development and Emerging Trends in the European Union. 5. Patient centricity and Precision Medicine. 6. Health Information Technology. 7. Digital Health Technologies and Innovations. 8. Economic Analysis and Outcome Assessment. 9. Partnerships and Collaborations. 10. Global Perspective: China Big Data Collaboration to Improve Patient Care. 11. The Future of Patient-Centric Data-Driven Healthcare
£99.75
Taylor & Francis Ltd Methodologies in Biosimilar Product Development
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£175.75
Taylor & Francis Ltd Toxicity and Risk Context Principles and Practice
Book SynopsisThis book aims to set out the political, social, legal and scientific underpinning of risk assessment and risk management for toxic substances. It describes the principles and processes the practitioners undertake when looking at the regulatory risk implications of their work.Table of ContentsPreface -- Acknowledgements -- 1 Introduction -- PART I -- The context in which toxic risk analysis takes place -- 2 What risk management covers -- 3 Legal and organisational frameworks -- 4 Philosophical frameworks for handling risk -- 5 The importance of risk perception and risk -- communication for toxicological risk assessment -- PART II -- The principles and practice of toxic risk analysis -- 6 Introduction: Royal Society and National Academy of Sciences -- 7 Toxicological assessment -- 8 Evaluation of human health effects: toxicity -- 9 Evaluation of human health effects: exposure -- 10 The special case of major accident hazards -- 11 Evaluation of effects on the environment -- 12 Effects on the atmosphere -- References -- Appendix -- Index.
£73.14
Taylor & Francis Ltd Statistical Thinking in Clinical Trials
Book SynopsisStatistical Thinking in Clinical Trials combines a relatively small number of key statistical principles and several instructive clinical trials to gently guide the reader through the statistical thinking needed in clinical trials. Randomization is the cornerstone of clinical trials and randomization-based inference is the cornerstone of this book. Read this book to learn the elegance and simplicity of re-randomization tests as the basis for statistical inference (the analyze as you randomize principle) and see how re-randomization tests can save a trial that required an unplanned, mid-course design change. Other principles enable the reader to quickly and confidently check calculations without relying on computer programs. The `EZ' principle says that a single sample size formula can be applied to a multitude of statistical tests. The `O minus E except after V' principle provides a simple estimator of the log odds ratio that is ideally suited for stratified
£50.34
Taylor & Francis Ltd Power and Sample Size in R
Book SynopsisPower and Sample Size in R guides the reader through power and sample size calculations for a wide variety of study outcomes and designs and illustrates their implementation in R software. It is designed to be used as a learning tool for students as well as a resource for experienced statisticians and investigators.The book begins by explaining the process of power calculation step by step at an introductory level and then builds to increasingly complex and varied topics. For each type of study design, the information needed to perform a calculation and the factors that affect power are explained. Concepts are explained with statistical rigor but made accessible through intuition and examples. Practical advice for performing sample size and power calculations for real studies is given throughout.The book demonstrates calculations in R. It is integrated with the companion R package powertools and also draws on and summarizes the capabilities of other R packages.
£71.24
Cambridge University Press Ethnicity in Drug Development and Therapeutics
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£33.24
Cambridge University Press Clinical Trials in Neurology
Book SynopsisClinical Trials in Neurology provides the tools to enhance the development of new treatments for neurologic diseases through introducing the reader to key concepts underpinning trials in the neurosciences. This comprehensive guide is essential reading for neurologists, psychiatrists, neurosurgeons, neuroscientists, statisticians and clinical researchers in the pharmaceutical industry.Table of ContentsPreface; Part I. The Role of Clinical Trials in Therapy Development: 1. The impact of clinical trials in neurology E. Ray Dorsey and S. Claiborne Johnston; 2. The sequence of clinical development Michael Poole; 3. Unique challenges in the development of therapies for neurological disorders Gilmore N. O'Neill; Part II. Concepts in Biostatistics and Clinical Measurement: 4. Fundamentals of biostatistics Judith Bebchuk and Janet Wittes; 5. Bias and random error Susan S. Ellenberg and Jacqueline A. French; 6. Approaches to data analysis William R. Clarke; 7. Selecting outcome measures Robert G. Holloway and Andrew D. Siderowf; Part III. Special Study Designs and Methods for Data Monitoring: 8. Selection and futility designs Bruce Levin; 9. Adaptive designs across stages of therapeutic development Christopher S. Coffey; 10. Crossover designs Mary E. Putt; 11. Two period designs for evaluation of disease-modifying treatments Michael P. McDermott; 12. Enrichment designs Kathryn M. Kellogg and John Markman; 13. Non-inferiority trials Rick Chappell; 14. Monitoring of clinical trials: interim monitoring, data monitoring committees, and group sequential methods applied to neurology Rickey E. Carter and Robert F. Woolson; 15. Clinical approaches to post-marketing drug safety assessment Gerald J. Dal Pan; Part IV. Ethical Issues: 16. Ethics in clinical trials involving the central nervous system: risk, benefit, justice, and integrity Jonathan Kimmelman; 17. The informed consent process: compliance and beyond Scott Y. H. Kim; Part V. Regulatory Perspectives: 18. Evidentiary standards for neurologic drugs and biologics approval Russell Katz; 19. Premarket reviews of neurological devices Eric A. Mann and Peter G. Como; Part VI. Clinical Trials in Common Neurological Disorders: 20. Parkinson's disease Karl D. Kieburtz and Jordan Elm; 21. Alzheimer's disease Joshua Grill and Jeffrey Cummings; 22. Acute ischemic stroke Karen C. Johnston, Devin L. Brown and Yuko Y. Palesch; 23. Multiple sclerosis Richard A. Rudick, Elizabeth Fisher and Gary R. Cutter; 24. Amytrophic lateral sclerosis (ALS) Nazem Atassi, David Schoenfeld and Merit Cudkowicz; 25. Epilepsy John R. Pollard, Susan S. Ellenberg and Jacqueline A. French; 26. Insomnia Michael E. Yurcheshen, Changyong Feng and J. Todd Arnedt; Part VII. Clinical Trial Planning and Implementation: 27. Clinical trial planning: an academic and industry perspective Cornelia L. Kamp and Jean-Michel Germain; 28. Clinical trial implementation, analysis and reporting: an academic and industry perspective Cornelia L. Kamp and Jean-Michel Germain; 29. Academic-industry collaborations and compliance issues Troy Morgan; Index.
£97.85
Cambridge University Press Antipsychotic Trials in Schizophrenia The CATIE Project Cambridge Medicine Hardcover
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£97.85
John Wiley and Sons Ltd Handbook for Clinical Trials of Imaging and
Book SynopsisThis book focuses on educating radiologists, radiation oncologists and others interested in imaging research about how to design and conduct clinical trials to evaluate imaging technology and imaging biomarkers.Table of ContentsContributors, vi Chapter 1 Imaging technology assessment, 1Pari V. Pandharipande and G. Scott Gazelle Chapter 2 Clinical trials of therapy, 10Sayeh Lavasani, Anthony F. Shields and Ali Mahinbakht Chapter 3 Clinical trials of image]guided interventions including radiotherapy studies, 29Gary S. Dorfman and Stephen M. Hahn Chapter 4 Imaging as a predictor of therapeutic response, 57David A. Mankoff and Anthony F. Shields Chapter 5 Screening trials and design, 76Janie M. Lee, Constance D. Lehman and Diana L. Miglioretti Chapter 6 Practicalities of running a clinical trial, 91Michael T. Lu, Elizabeth C. Adami and Udo Hoffmann Chapter 7 Statistical issues in study design, 103Nancy A. Obuchowski Chapter 8 Introduction to biostatistical methods, 126Diana L. Miglioretti, Todd A. Alonzo and Nancy A. Obuchowski Chapter 9 Methods for studies of diagnostic tests, 147Jeffrey D. Blume Chapter 10 Methods for quantitative imaging biomarker studies, 170Alicia Y. Toledano and Nancy A. Obuchowski Chapter 11 Introduction to cost]effectiveness analysis in clinical trials, 189Ruth C. Carlos and G. Scott Gazelle Index, 208
£82.60
John Wiley & Sons Inc Crossover Designs
Book SynopsisA comprehensive and practical resource for analyses of crossover designs For ethical reasons, it isvital to keep the number of patients in a clinical trial aslow as possible.As evidenced by extensive research publications, crossover designcan bea useful and powerfultool to reduce the number of patients needed for a parallel group design in studying treatmentsfor non-curable chronic diseases. This book introduces commonly-used and well-established statistical tests and estimators in epidemiology that can easily be applied to hypothesis testing and estimation of the relative treatment effect for various types of data scale in crossover designs. Models with distribution-free random effects are assumed and hence most approaches considered here are semi-parametric. The book provides clinicians and biostatisticians with the exact test procedures and exact interval estimators, which are applicable even when the number of patients in a crossover trial is small. SystematTable of ContentsAbout the author xi Preface xii About the companion website xiv 1 Crossover design – definitions, notes, and limitations 1 1.1 Unsuitability for acute or most infectious diseases 2 1.2 Inappropriateness for treatments with long-lasting effects 2 1.3 Loss of efficiency in the presence of carry-over effects 3 1.4 Concerns of treatment-by-period interaction 3 1.5 Flaw of the commonly used two-stage test procedure 4 1.6 Higher risk of dropping out or being lost to follow-up 4 1.7 More assumptions needed in use of a crossover design 5 1.8 General principle and conditional approach used in the book 5 2 AB/BA design in continuous data 7 2.1 Testing non-equality of treatments 10 2.2 Testing non-inferiority of an experimental treatment to an active control treatment 11 2.3 Testing equivalence between an experimental treatment and an active control treatment 12 2.4 Interval estimation of the mean difference 13 2.5 Sample size determination 16 2.5.1 Sample size for testing non-equality 16 2.5.2 Sample size for testing non-inferiority 17 2.5.3 Sample size for testing equivalence 18 2.6 Hypothesis testing and estimation for the period effect 19 2.7 Estimation of the relative treatment effect in the presence of differential carry-over effects 21 2.8 Examples of SAS programs and results 22 Exercises 27 3 AB/BA design in dichotomous data 30 3.1 Testing non-equality of treatments 34 3.2 Testing non-inferiority of an experimental treatment to an active control treatment 36 3.3 Testing equivalence between an experimental treatment and an active control treatment 39 3.4 Interval estimation of the odds ratio 40 3.5 Sample size determination 42 3.5.1 Sample size for testing non-equality 42 3.5.2 Sample size for testing non-inferiority 42 3.5.3 Sample size for testing equivalence 43 3.6 Hypothesis testing and estimation for the period effect 45 3.7 Testing and estimation for carry-over effects 47 3.8 SAS program codes and likelihood-based approach 48 Exercises 51 4 AB/BA design in ordinal data 57 4.1 Testing non-equality of treatments 62 4.2 Testing non-inferiority of an experimental treatment to an active control treatment 64 4.3 Testing equivalence between an experimental treatment and an active control treatment 65 4.4 Interval estimation of the generalized odds ratio 66 4.5 Sample size determination 67 4.5.1 Sample size for testing non-equality 67 4.5.2 Sample size for testing non-inferiority 68 4.5.3 Sample size for testing equivalence 68 4.6 Hypothesis testing and estimation for the period effect 70 4.7 SAS codes for the proportional odds model with normal random effects 72 Exercises 74 5 AB/BA design in frequency data 75 5.1 Testing non-equality of treatments 78 5.2 Testing non-inferiority of an experimental treatment to an active control treatment 81 5.3 Testing equivalence between an experimental treatment and an active control treatment 83 5.4 Interval estimation of the ratio of mean frequencies 84 5.5 Sample size determination 86 5.5.1 Sample size for testing non-equality 86 5.5.2 Sample size for testing non-inferiority 87 5.5.3 Sample size for testing equivalence 88 5.6 Hypothesis testing and estimation for the period effect 88 5.7 Estimation of the relative treatment effect in the presence of differential carry-over effects 90 Exercises 92 6 Three-treatment three-period crossover design in continuous data 95 6.1 Testing non-equality between treatments and placebo 102 6.2 Testing non-inferiority of an experimental treatment to an active control treatment 103 6.3 Testing equivalence between an experimental treatment and an active control treatment 104 6.4 Interval estimation of the mean difference 104 6.5 Hypothesis testing and estimation for period effects 105 6.6 Procedures for testing treatment-by-period interactions 107 6.7 SAS program codes and results for constant variance 109 Exercises 111 7 Three-treatment three-period crossover design in dichotomous data 115 7.1 Testing non-equality of treatments 121 7.1.1 Asymptotic test procedures 121 7.1.2 Exact test procedures 123 7.1.3 Procedures for simultaneously testing non-equality of two experimental treatments versus a placebo 124 7.2 Testing non-inferiority of an experimental treatment to an active control treatment 126 7.3 Testing equivalence between an experimental treatment and an active control treatment 127 7.4 Interval estimation of the odds ratio 129 7.5 Hypothesis testing and estimation for period effects 131 7.6 Procedures for testing treatment-by-period interactions 133 7.7 SAS program codes and results for a logistic regression model with normal random effects 136 Exercises 138 8 Three-treatment three-period crossover design in ordinal data 141 8.1 Testing non-equality of treatments 150 8.1.1 Asymptotic test procedures 150 8.1.2 Exact test procedure 152 8.2 Testing non-inferiority of an experimental treatment to an active control treatment 153 8.3 Testing equivalence between an experimental treatment and an active control treatment 153 8.4 Interval estimation of the GOR 154 8.5 Hypothesis testing and estimation for period effects 156 8.6 Procedures for testing treatment-by-period interactions 159 8.7 SAS program codes and results for the proportional odds model with normal random effects 160 Exercises 162 9 Three-treatment three-period crossover design in frequency data 164 9.1 Testing non-equality between treatments and placebo 170 9.2 Testing non-inferiority of an experimental treatment to an active control treatment 173 9.3 Testing equivalence between an experimental treatment and an active control treatment 174 9.4 Interval estimation of the ratio of mean frequencies 175 9.5 Hypothesis testing and estimation for period effects 178 9.6 Procedures for testing treatment-by-period interactions 179 Exercises 181 10 Three-treatment (incomplete block) crossover design in continuous and dichotomous data 183 10.1 Continuous data 185 10.1.1 Testing non-equality of treatments 188 10.1.2 Testing non-equality between experimental treatments (or non-nullity of dose effects) 189 10.1.3 Interval estimation of the mean difference 190 10.1.4 SAS codes for fixed effects and mixed effects models 192 10.2 Dichotomous data 194 10.2.1 Testing non-equality of treatments 197 10.2.2 Testing non-equality between experimental treatments (or non-nullity of dose effects) 199 10.2.3 Testing non-inferiority of either experimental treatment to an active control treatment 199 10.2.4 Interval estimation of the odds ratio 200 10.2.5 SAS codes for the likelihood-based approach 202 Exercises 203 References 208 Index 216
£73.95
