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Presents elements of clinical trial methods that are essential in planning, designing, conducting, analyzing, and interpreting clinical trials with the goal of improving the evidence derived from these important studies
This Third Edition builds on the text's reputation as a straightforward, detailed, and authoritative presentation of quantitative methods for clinical trials. Readers will encounter the principles of design for various types of clinical trials, and are then skillfully guided through the complete process of planning the experiment, assembling a study cohort, assessing data, and reporting results. Throughout the process, the author alerts readers to problems that may arise during the course of the trial and provides common sense solutions. All stages of therapeutic development are discussed in detail, and the methods are not restricted to a single clinical application area.
The authors bases current revisions and updates on his own experience, classroom instruction, and feedback from teachers and medical and statistical professionals involved in clinical trials. The Third Edition greatly expands its coverage, ranging from statistical principles to new and provocative topics, including alternative medicine and ethics, middle development, comparative studies, and adaptive designs. At the same time, it offers more pragmatic advice for issues such as selecting outcomes, sample size, analysis, reporting, and handling allegations of misconduct. Readers familiar with the First and Second Editions will discover revamped exercise sets; an updated and extensive reference section; new material on endpoints and the developmental pipeline, among others; and revisions of numerous sections.
In addition, this book:
* Features accessible and broad coverage of statistical design methods--the crucial building blocks of clinical trials and medical research -- now complete with new chapters on overall development, middle development, comparative studies, and adaptive designs
* Teaches readers to design clinical trials that produce valid qualitative results backed by rigorous statistical methods
* Contains an introduction and summary in each chapter to reinforce key points
* Includes discussion questions to stimulate critical thinking and help readers understand how they can apply their newfound knowledge
* Provides extensive references to direct readers to the most recent literature, and there are numerous new or revised exercises throughout the book
Clinical Trials: A Methodologic Perspective, Third Edition is a textbook accessible to advanced undergraduate students in the quantitative sciences, graduate students in public health and the life sciences, physicians training in clinical research methods, and biostatisticians and epidemiologists.
Steven Piantadosi, MD, PhD, is the Phase One Foundation Distinguished Chair and Director of the Samuel Oschin Cancer Institute, and Professor of Medicine at Cedars-Sinai Medical Center in Los Angeles, California. Dr. Piantadosi is one of the world's leading experts in the design and analysis of clinical trials for cancer research. He has taught clinical trials methods extensively in formal courses and short venues. He has advised numerous academic programs and collaborations nationally regarding clinical trial design and conduct, and has served on external advisory boards for the National Institutes of Health and other prominent cancer programs and centers. The author of more than 260 peer-reviewed scientific articles, Dr. Piantadosi has published extensively on research results, clinical applications, and trial methodology. While his papers have contributed to many areas of oncology, he has also collaborated on diverse studies outside oncology including lung disease and degenerative neurological disease.
This Third Edition builds on the text's reputation as a straightforward, detailed, and authoritative presentation of quantitative methods for clinical trials. Readers will encounter the principles of design for various types of clinical trials, and are then skillfully guided through the complete process of planning the experiment, assembling a study cohort, assessing data, and reporting results. Throughout the process, the author alerts readers to problems that may arise during the course of the trial and provides common sense solutions. All stages of therapeutic development are discussed in detail, and the methods are not restricted to a single clinical application area.
The authors bases current revisions and updates on his own experience, classroom instruction, and feedback from teachers and medical and statistical professionals involved in clinical trials. The Third Edition greatly expands its coverage, ranging from statistical principles to new and provocative topics, including alternative medicine and ethics, middle development, comparative studies, and adaptive designs. At the same time, it offers more pragmatic advice for issues such as selecting outcomes, sample size, analysis, reporting, and handling allegations of misconduct. Readers familiar with the First and Second Editions will discover revamped exercise sets; an updated and extensive reference section; new material on endpoints and the developmental pipeline, among others; and revisions of numerous sections.
In addition, this book:
* Features accessible and broad coverage of statistical design methods--the crucial building blocks of clinical trials and medical research -- now complete with new chapters on overall development, middle development, comparative studies, and adaptive designs
* Teaches readers to design clinical trials that produce valid qualitative results backed by rigorous statistical methods
* Contains an introduction and summary in each chapter to reinforce key points
* Includes discussion questions to stimulate critical thinking and help readers understand how they can apply their newfound knowledge
* Provides extensive references to direct readers to the most recent literature, and there are numerous new or revised exercises throughout the book
Clinical Trials: A Methodologic Perspective, Third Edition is a textbook accessible to advanced undergraduate students in the quantitative sciences, graduate students in public health and the life sciences, physicians training in clinical research methods, and biostatisticians and epidemiologists.
Steven Piantadosi, MD, PhD, is the Phase One Foundation Distinguished Chair and Director of the Samuel Oschin Cancer Institute, and Professor of Medicine at Cedars-Sinai Medical Center in Los Angeles, California. Dr. Piantadosi is one of the world's leading experts in the design and analysis of clinical trials for cancer research. He has taught clinical trials methods extensively in formal courses and short venues. He has advised numerous academic programs and collaborations nationally regarding clinical trial design and conduct, and has served on external advisory boards for the National Institutes of Health and other prominent cancer programs and centers. The author of more than 260 peer-reviewed scientific articles, Dr. Piantadosi has published extensively on research results, clinical applications, and trial methodology. While his papers have contributed to many areas of oncology, he has also collaborated on diverse studies outside oncology including lung disease and degenerative neurological disease.
Presents elements of clinical trial methods that are essential in planning, designing, conducting, analyzing, and interpreting clinical trials with the goal of improving the evidence derived from these important studies
This Third Edition builds on the text's reputation as a straightforward, detailed, and authoritative presentation of quantitative methods for clinical trials. Readers will encounter the principles of design for various types of clinical trials, and are then skillfully guided through the complete process of planning the experiment, assembling a study cohort, assessing data, and reporting results. Throughout the process, the author alerts readers to problems that may arise during the course of the trial and provides common sense solutions. All stages of therapeutic development are discussed in detail, and the methods are not restricted to a single clinical application area.
The authors bases current revisions and updates on his own experience, classroom instruction, and feedback from teachers and medical and statistical professionals involved in clinical trials. The Third Edition greatly expands its coverage, ranging from statistical principles to new and provocative topics, including alternative medicine and ethics, middle development, comparative studies, and adaptive designs. At the same time, it offers more pragmatic advice for issues such as selecting outcomes, sample size, analysis, reporting, and handling allegations of misconduct. Readers familiar with the First and Second Editions will discover revamped exercise sets; an updated and extensive reference section; new material on endpoints and the developmental pipeline, among others; and revisions of numerous sections.
In addition, this book:
* Features accessible and broad coverage of statistical design methods--the crucial building blocks of clinical trials and medical research -- now complete with new chapters on overall development, middle development, comparative studies, and adaptive designs
* Teaches readers to design clinical trials that produce valid qualitative results backed by rigorous statistical methods
* Contains an introduction and summary in each chapter to reinforce key points
* Includes discussion questions to stimulate critical thinking and help readers understand how they can apply their newfound knowledge
* Provides extensive references to direct readers to the most recent literature, and there are numerous new or revised exercises throughout the book
Clinical Trials: A Methodologic Perspective, Third Edition is a textbook accessible to advanced undergraduate students in the quantitative sciences, graduate students in public health and the life sciences, physicians training in clinical research methods, and biostatisticians and epidemiologists.
Steven Piantadosi, MD, PhD, is the Phase One Foundation Distinguished Chair and Director of the Samuel Oschin Cancer Institute, and Professor of Medicine at Cedars-Sinai Medical Center in Los Angeles, California. Dr. Piantadosi is one of the world's leading experts in the design and analysis of clinical trials for cancer research. He has taught clinical trials methods extensively in formal courses and short venues. He has advised numerous academic programs and collaborations nationally regarding clinical trial design and conduct, and has served on external advisory boards for the National Institutes of Health and other prominent cancer programs and centers. The author of more than 260 peer-reviewed scientific articles, Dr. Piantadosi has published extensively on research results, clinical applications, and trial methodology. While his papers have contributed to many areas of oncology, he has also collaborated on diverse studies outside oncology including lung disease and degenerative neurological disease.
This Third Edition builds on the text's reputation as a straightforward, detailed, and authoritative presentation of quantitative methods for clinical trials. Readers will encounter the principles of design for various types of clinical trials, and are then skillfully guided through the complete process of planning the experiment, assembling a study cohort, assessing data, and reporting results. Throughout the process, the author alerts readers to problems that may arise during the course of the trial and provides common sense solutions. All stages of therapeutic development are discussed in detail, and the methods are not restricted to a single clinical application area.
The authors bases current revisions and updates on his own experience, classroom instruction, and feedback from teachers and medical and statistical professionals involved in clinical trials. The Third Edition greatly expands its coverage, ranging from statistical principles to new and provocative topics, including alternative medicine and ethics, middle development, comparative studies, and adaptive designs. At the same time, it offers more pragmatic advice for issues such as selecting outcomes, sample size, analysis, reporting, and handling allegations of misconduct. Readers familiar with the First and Second Editions will discover revamped exercise sets; an updated and extensive reference section; new material on endpoints and the developmental pipeline, among others; and revisions of numerous sections.
In addition, this book:
* Features accessible and broad coverage of statistical design methods--the crucial building blocks of clinical trials and medical research -- now complete with new chapters on overall development, middle development, comparative studies, and adaptive designs
* Teaches readers to design clinical trials that produce valid qualitative results backed by rigorous statistical methods
* Contains an introduction and summary in each chapter to reinforce key points
* Includes discussion questions to stimulate critical thinking and help readers understand how they can apply their newfound knowledge
* Provides extensive references to direct readers to the most recent literature, and there are numerous new or revised exercises throughout the book
Clinical Trials: A Methodologic Perspective, Third Edition is a textbook accessible to advanced undergraduate students in the quantitative sciences, graduate students in public health and the life sciences, physicians training in clinical research methods, and biostatisticians and epidemiologists.
Steven Piantadosi, MD, PhD, is the Phase One Foundation Distinguished Chair and Director of the Samuel Oschin Cancer Institute, and Professor of Medicine at Cedars-Sinai Medical Center in Los Angeles, California. Dr. Piantadosi is one of the world's leading experts in the design and analysis of clinical trials for cancer research. He has taught clinical trials methods extensively in formal courses and short venues. He has advised numerous academic programs and collaborations nationally regarding clinical trial design and conduct, and has served on external advisory boards for the National Institutes of Health and other prominent cancer programs and centers. The author of more than 260 peer-reviewed scientific articles, Dr. Piantadosi has published extensively on research results, clinical applications, and trial methodology. While his papers have contributed to many areas of oncology, he has also collaborated on diverse studies outside oncology including lung disease and degenerative neurological disease.
Über den Autor
Steven Piantadosi, MD, PhD, is the Phase One Foundation Distinguished Chair and Director of the Samuel Oschin Cancer Institute, and Professor of Medicine at Cedars-Sinai Medical Center in Los Angeles, California. Dr. Piantadosi is one of the world's leading experts in the design and analysis of clinical trials for cancer research. He has taught clinical trial methods extensively in formal courses and short venues. He has advised numerous academic programs and collaborations nationally regarding clinical trial design and conduct, and has served on external advisory boards for the National Institutes of Health and other prominent cancer programs and centers. The author of more than 260 peer-reviewed scientific articles, Dr. Piantadosi has published extensively on research results, clinical applications, and trial methodology. While his papers have contributed to many areas of oncology, he has also collaborated on diverse studies outside oncology including lung disease and degenerative neurological disease.
Inhaltsverzeichnis
Preface to the Third Edition xxv
About the Companion Website xxviii
1 Preliminaries 1
1.1 Introduction, 1
1.2 Audiences, 2
1.3 Scope, 3
1.4 Other Sources of Knowledge, 5
1.5 Notation and Terminology, 6
1.5.1 Clinical Trial Terminology, 7
1.5.2 Drug Development Traditionally Recognizes Four Trial Design Types, 7
1.5.3 Descriptive Terminology Is Better, 8
1.6 Examples, Data, and Programs, 9
1.7 Summary, 9
2 Clinical Trials as Research 10
2.1 Introduction, 10
2.2 Research, 13
2.2.1 What Is Research?, 13
2.2.2 Clinical Reasoning Is Based on the Case History, 14
2.2.3 Statistical Reasoning Emphasizes Inference Based on Designed Data Production, 16
2.2.4 Clinical and Statistical Reasoning Converge in Research, 17
2.3 Defining Clinical Trials, 19
2.3.1 Mixing of Clinical and Statistical Reasoning Is Recent, 19
2.3.2 Clinical Trials Are Rigorously Defined, 21
2.3.3 Theory and Data, 22
2.3.4 Experiments Can Be Misunderstood, 23
2.3.5 Clinical Trials and the Frankenstein Myth, 25
2.3.6 Cavia porcellus, 26
2.3.7 Clinical Trials as Science, 26
2.3.8 Trials and Statistical Methods Fit within a Spectrum of Clinical Research, 28
2.4 Practicalities of Usage, 29
2.4.1 Predicates for a Trial, 29
2.4.2 Trials Can Provide Confirmatory Evidence, 29
2.4.3 Clinical Trials Are Reliable Albeit Unwieldy and Messy, 30
2.4.4 Trials Are Difficult to Apply in Some Circumstances, 31
2.4.5 Randomized Studies Can Be Initiated Early, 32
2.4.6 What Can I learn from = 20?, 33
2.5 Nonexperimental Designs, 35
2.5.1 Other Methods Are Valid forMaking Some Clinical Inferences, 35
2.5.2 Some Specific Nonexperimental Designs, 38
2.5.3 Causal Relationships, 40
2.5.4 Will Genetic Determinism Replace Design?, 41
2.6 Summary, 41
2.7 Questions for Discussion, 41
3 Why Clinical Trials Are Ethical 43
3.1 Introduction, 43
3.1.1 Science and Ethics Share Objectives, 44
3.1.2 Equipoise and Uncertainty, 46
3.2 Duality, 47
3.2.1 Clinical Trials Sharpen, But Do Not Create, Duality, 47
3.2.2 A Gene Therapy Tragedy Illustrates Duality, 48
3.2.3 Research and Practice Are Convergent, 48
3.2.4 Hippocratic Tradition Does Not Proscribe Clinical Trials, 52
3.2.5 Physicians Always Have Multiple Roles, 54
3.3 Historically Derived Principles of Ethics, 57
3.3.1 Nuremberg Contributed an Awareness of the Worst Problems, 57
3.3.2 High-Profile Mistakes Were Made in the United States, 58
3.3.3 The Helsinki Declaration Was Widely Adopted, 58
3.3.4 Other International Guidelines Have Been Proposed, 61
3.3.5 Institutional Review Boards Provide Ethics Oversight, 62
3.3.6 Ethics Principles Relevant to Clinical Trials, 63
3.4 Contemporary Foundational Principles, 65
3.4.1 Collaborative Partnership, 66
3.4.2 Scientific Value, 66
3.4.3 Scientific Validity, 66
3.4.4 Fair Subject Selection, 67
3.4.5 Favorable Risk-Benefit, 67
3.4.6 Independent Review, 68
3.4.7 Informed Consent, 68
3.4.8 Respect for Subjects, 71
3.5 Methodologic Reflections, 72
3.5.1 Practice Based on Unproven Treatments Is Not Ethical, 72
3.5.2 Ethics Considerations Are Important Determinants of Design, 74
3.5.3 Specific Methods Have Justification, 75
3.6 Professional Conduct, 79
3.6.1 Advocacy, 79
3.6.2 Physician to Physician Communication Is Not Research, 81
3.6.3 Investigator Responsibilities, 82
3.6.4 Professional Ethics, 83
3.7 Summary, 85
3.8 Questions for Discussion, 86
4 Contexts for Clinical Trials 87
4.1 Introduction, 87
4.1.1 Clinical Trial Registries, 88
4.1.2 Public Perception Versus Science, 90
4.2 Drugs, 91
4.2.1 Are Drugs Special?, 92
4.2.2 Why Trials Are Used Extensively for Drugs, 93
4.3 Devices, 95
4.3.1 Use of Trials for Medical Devices, 95
4.3.2 Are Devices Different from Drugs?, 97
4.3.3 Case Study, 98
4.4 Prevention, 99
4.4.1 The Prevention versus Therapy Dichotomy Is Over-worked, 100
4.4.2 Vaccines and Biologicals, 101
4.4.3 Ebola 2014 and Beyond, 102
4.4.4 A Perspective on Risk-Benefit, 103
4.4.5 Methodology and Framework for Prevention Trials, 105
4.5 Complementary and Alternative Medicine, 106
4.5.1 Science Is the Study of Natural Phenomena, 108
4.5.2 Ignorance Is Important, 109
4.5.3 The Essential Paradox of CAM and Clinical Trials, 110
4.5.4 Why Trials Have Not Been Used Extensively in CAM, 111
4.5.5 Some Principles for Rigorous Evaluation, 113
4.5.6 Historic Examples, 115
4.6 Surgery and Skill-Dependent Therapies, 116
4.6.1 Why Trials Have Been Used Less Extensively in Surgery, 118
4.6.2 Reasons Why Some Surgical Therapies Require Less Rigorous Study Designs, 120
4.6.3 Sources of Variation, 121
4.6.4 Difficulties of Inference, 121
4.6.5 Control of Observer Bias Is Possible, 122
4.6.6 Illustrations from an Emphysema Surgery Trial, 124
4.7 A Brief View of Some Other Contexts, 130
4.7.1 Screening Trials, 130
4.7.2 Diagnostic Trials, 134
4.7.3 Radiation Therapy, 134
4.8 Summary, 135
4.9 Questions for Discussion, 136
5 Measurement 137
5.1 Introduction, 137
5.1.1 Types of Uncertainty, 138
5.2 Objectives, 140
5.2.1 Estimation Is The Most Common Objective, 141
5.2.2 Selection Can Also Be an Objective, 141
5.2.3 Objectives Require Various Scales of Measurement, 142
5.3 Measurement Design, 143
5.3.1 Mixed Outcomes and Predictors, 143
5.3.2 Criteria for Evaluating Outcomes, 144
5.3.3 Prefer Hard or Objective Outcomes, 145
5.3.4 Outcomes Can Be Quantitative or Qualitative, 146
5.3.5 Measures Are Useful and Efficient Outcomes, 146
5.3.6 Some Outcomes Are Summarized as Counts, 147
5.3.7 Ordered Categories Are Commonly Used for Severity or Toxicity, 147
5.3.8 Unordered Categories Are Sometimes Used, 148
5.3.9 Dichotomies Are Simple Summaries, 148
5.3.10 Measures of Risk, 149
5.3.11 Primary and Others, 153
5.3.12 Composites, 154
5.3.13 Event Times and Censoring, 155
5.3.14 Longitudinal Measures, 160
5.3.15 Central Review, 161
5.3.16 Patient Reported Outcomes, 161
5.4 Surrogate Outcomes, 162
5.4.1 Surrogate Outcomes Are Disease-Specific, 164
5.4.2 Surrogate Outcomes Can Make Trials More Efficient, 167
5.4.3 Surrogate Outcomes Have Significant Limitations, 168
5.5 Summary, 170
5.6 Questions for Discussion, 171
6 Random Error and Bias 172
6.1 Introduction, 172
6.1.1 The Effects of Random and Systematic Errors Are Distinct, 173
6.1.2 Hypothesis Tests versus Significance Tests, 174
6.1.3 Hypothesis Tests Are Subject to Two Types of Random Error, 175
6.1.4 Type I Errors Are Relatively Easy to Control, 176
6.1.5 The Properties of Confidence IntervalsAre Similar toHypothesis Tests, 176
6.1.6 Using a one- or two-sided hypothesis test is not the right question, 177
6.1.7 P-Values Quantify the Type I Error, 178
6.1.8 Type II Errors Depend on the Clinical Difference of Interest, 178
6.1.9 Post Hoc Power Calculations Are Useless, 180
6.2 Clinical Bias, 181
6.2.1 Relative Size of Random Error and Bias is Important, 182
6.2.2 Bias Arises from Numerous Sources, 182
6.2.3 Controlling Structural Bias is Conceptually Simple, 185
6.3 Statistical Bias, 188
6.3.1 Selection Bias, 188
6.3.2 Some Statistical Bias Can Be Corrected, 192
6.3.3 Unbiasedness is Not the Only Desirable Attribute of an Estimator, 192
6.4 Summary, 194
6.5 Questions for Discussion, 194
7 Statistical Perspectives 196
7.1 Introduction, 196
7.2 Differences in Statistical Perspectives, 197
7.2.1 Models and Parameters, 197
7.2.2 Philosophy of Inference Divides Statisticians, 198
7.2.3 Resolution, 199
7.2.4 Points of Agreement, 199
7.3 Frequentist, 202
7.3.1 Binomial Case Study, 203
7.3.2 Other Issues, 204
7.4 Bayesian, 204
7.4.1 Choice of a Prior Distribution Is a Source of Contention, 205
7.4.2 Binomial Case Study, 206
7.4.3 Bayesian Inference Is Different, 209
7.5 Likelihood, 210
7.5.1 Binomial Case Study, 211
7.5.2 Likelihood-Based Design, 211
7.6 Statistics Issues, 212
7.6.1 Perspective, 212
7.6.2 Statistical Procedures Are Not Standardized, 213
7.6.3 Practical Controversies Related to Statistics Exist, 214
7.7 Summary, 215
7.8 Questions for Discussion, 216
8 Experiment Design in Clinical Trials 217
8.1 Introduction, 217
8.2 Trials As Simple Experiment Designs, 218
8.2.1 Design Space Is Chaotic, 219
8.2.2 Design Is Critical for Inference, 220
8.2.3 The Question Drives the Design, 220
8.2.4 Design Depends on the Observation Model As Well As the
Biological Question, 221
8.2.5 Comparing Designs, 222
8.3 Goals of Experiment Design, 223
8.3.1 Control of Random Error and Bias Is the Goal, 223
8.3.2 Conceptual Simplicity Is Also a Goal, 223
8.3.3 Encapsulation of Subjectivity, 224
8.3.4 Leech Case Study, 225
8.4 Design Concepts, 225
8.4.1 The Foundations of Design Are Observation and Theory, 226
8.4.2 A Lesson from the Women's Health Initiative, 227
8.4.3 Experiments Use Three Components of Design, 229
8.5 Design Features, 230
8.5.1 Enrichment, 231
8.5.2 Replication, 232
8.5.3 Experimental and Observational Units, 232
8.5.4 Treatments and Factors, 233
8.5.5 Nesting, 233
8.5.6 Randomization, 234
8.5.7 Blocking, 234
8.5.8 Stratification, 235
8.5.9 Masking, 236
8.6 Special Design Issues, 237
8.6.1 Placebos, 237
8.6.2 Equivalence and Noninferiority, 240
8.6.3 Randomized Discontinuation, 241
8.6.4 Hybrid Designs May Be Needed for Resolving Special Questions, 242
8.6.5 Clinical Trials Cannot Meet Certain Objectives, 242
8.7 Importance of the Protocol Document, 244
8.7.1 Protocols Have Many Functions, 244
8.7.2 Deviations from Protocol Specifications are Common, 245
8.7.3 Protocols Are Structured, Logical, and Complete, 246
8.8 Summary, 252
8.9 Questions for Discussion, 253
9 The Trial Cohort 254
9.1 Introduction, 254
9.2 Cohort Definition and Selection, 255
9.2.1 Eligibility and Exclusions, 255
9.2.2 Active Sampling and Enrichment, 257
9.2.3 Participation may select subjects with better prognosis, 258
9.2.4 Quantitative Selection Criteria Versus False Precision, 262
9.2.5 Comparative Trials Are Not Sensitive to Selection, 263
9.3...
About the Companion Website xxviii
1 Preliminaries 1
1.1 Introduction, 1
1.2 Audiences, 2
1.3 Scope, 3
1.4 Other Sources of Knowledge, 5
1.5 Notation and Terminology, 6
1.5.1 Clinical Trial Terminology, 7
1.5.2 Drug Development Traditionally Recognizes Four Trial Design Types, 7
1.5.3 Descriptive Terminology Is Better, 8
1.6 Examples, Data, and Programs, 9
1.7 Summary, 9
2 Clinical Trials as Research 10
2.1 Introduction, 10
2.2 Research, 13
2.2.1 What Is Research?, 13
2.2.2 Clinical Reasoning Is Based on the Case History, 14
2.2.3 Statistical Reasoning Emphasizes Inference Based on Designed Data Production, 16
2.2.4 Clinical and Statistical Reasoning Converge in Research, 17
2.3 Defining Clinical Trials, 19
2.3.1 Mixing of Clinical and Statistical Reasoning Is Recent, 19
2.3.2 Clinical Trials Are Rigorously Defined, 21
2.3.3 Theory and Data, 22
2.3.4 Experiments Can Be Misunderstood, 23
2.3.5 Clinical Trials and the Frankenstein Myth, 25
2.3.6 Cavia porcellus, 26
2.3.7 Clinical Trials as Science, 26
2.3.8 Trials and Statistical Methods Fit within a Spectrum of Clinical Research, 28
2.4 Practicalities of Usage, 29
2.4.1 Predicates for a Trial, 29
2.4.2 Trials Can Provide Confirmatory Evidence, 29
2.4.3 Clinical Trials Are Reliable Albeit Unwieldy and Messy, 30
2.4.4 Trials Are Difficult to Apply in Some Circumstances, 31
2.4.5 Randomized Studies Can Be Initiated Early, 32
2.4.6 What Can I learn from = 20?, 33
2.5 Nonexperimental Designs, 35
2.5.1 Other Methods Are Valid forMaking Some Clinical Inferences, 35
2.5.2 Some Specific Nonexperimental Designs, 38
2.5.3 Causal Relationships, 40
2.5.4 Will Genetic Determinism Replace Design?, 41
2.6 Summary, 41
2.7 Questions for Discussion, 41
3 Why Clinical Trials Are Ethical 43
3.1 Introduction, 43
3.1.1 Science and Ethics Share Objectives, 44
3.1.2 Equipoise and Uncertainty, 46
3.2 Duality, 47
3.2.1 Clinical Trials Sharpen, But Do Not Create, Duality, 47
3.2.2 A Gene Therapy Tragedy Illustrates Duality, 48
3.2.3 Research and Practice Are Convergent, 48
3.2.4 Hippocratic Tradition Does Not Proscribe Clinical Trials, 52
3.2.5 Physicians Always Have Multiple Roles, 54
3.3 Historically Derived Principles of Ethics, 57
3.3.1 Nuremberg Contributed an Awareness of the Worst Problems, 57
3.3.2 High-Profile Mistakes Were Made in the United States, 58
3.3.3 The Helsinki Declaration Was Widely Adopted, 58
3.3.4 Other International Guidelines Have Been Proposed, 61
3.3.5 Institutional Review Boards Provide Ethics Oversight, 62
3.3.6 Ethics Principles Relevant to Clinical Trials, 63
3.4 Contemporary Foundational Principles, 65
3.4.1 Collaborative Partnership, 66
3.4.2 Scientific Value, 66
3.4.3 Scientific Validity, 66
3.4.4 Fair Subject Selection, 67
3.4.5 Favorable Risk-Benefit, 67
3.4.6 Independent Review, 68
3.4.7 Informed Consent, 68
3.4.8 Respect for Subjects, 71
3.5 Methodologic Reflections, 72
3.5.1 Practice Based on Unproven Treatments Is Not Ethical, 72
3.5.2 Ethics Considerations Are Important Determinants of Design, 74
3.5.3 Specific Methods Have Justification, 75
3.6 Professional Conduct, 79
3.6.1 Advocacy, 79
3.6.2 Physician to Physician Communication Is Not Research, 81
3.6.3 Investigator Responsibilities, 82
3.6.4 Professional Ethics, 83
3.7 Summary, 85
3.8 Questions for Discussion, 86
4 Contexts for Clinical Trials 87
4.1 Introduction, 87
4.1.1 Clinical Trial Registries, 88
4.1.2 Public Perception Versus Science, 90
4.2 Drugs, 91
4.2.1 Are Drugs Special?, 92
4.2.2 Why Trials Are Used Extensively for Drugs, 93
4.3 Devices, 95
4.3.1 Use of Trials for Medical Devices, 95
4.3.2 Are Devices Different from Drugs?, 97
4.3.3 Case Study, 98
4.4 Prevention, 99
4.4.1 The Prevention versus Therapy Dichotomy Is Over-worked, 100
4.4.2 Vaccines and Biologicals, 101
4.4.3 Ebola 2014 and Beyond, 102
4.4.4 A Perspective on Risk-Benefit, 103
4.4.5 Methodology and Framework for Prevention Trials, 105
4.5 Complementary and Alternative Medicine, 106
4.5.1 Science Is the Study of Natural Phenomena, 108
4.5.2 Ignorance Is Important, 109
4.5.3 The Essential Paradox of CAM and Clinical Trials, 110
4.5.4 Why Trials Have Not Been Used Extensively in CAM, 111
4.5.5 Some Principles for Rigorous Evaluation, 113
4.5.6 Historic Examples, 115
4.6 Surgery and Skill-Dependent Therapies, 116
4.6.1 Why Trials Have Been Used Less Extensively in Surgery, 118
4.6.2 Reasons Why Some Surgical Therapies Require Less Rigorous Study Designs, 120
4.6.3 Sources of Variation, 121
4.6.4 Difficulties of Inference, 121
4.6.5 Control of Observer Bias Is Possible, 122
4.6.6 Illustrations from an Emphysema Surgery Trial, 124
4.7 A Brief View of Some Other Contexts, 130
4.7.1 Screening Trials, 130
4.7.2 Diagnostic Trials, 134
4.7.3 Radiation Therapy, 134
4.8 Summary, 135
4.9 Questions for Discussion, 136
5 Measurement 137
5.1 Introduction, 137
5.1.1 Types of Uncertainty, 138
5.2 Objectives, 140
5.2.1 Estimation Is The Most Common Objective, 141
5.2.2 Selection Can Also Be an Objective, 141
5.2.3 Objectives Require Various Scales of Measurement, 142
5.3 Measurement Design, 143
5.3.1 Mixed Outcomes and Predictors, 143
5.3.2 Criteria for Evaluating Outcomes, 144
5.3.3 Prefer Hard or Objective Outcomes, 145
5.3.4 Outcomes Can Be Quantitative or Qualitative, 146
5.3.5 Measures Are Useful and Efficient Outcomes, 146
5.3.6 Some Outcomes Are Summarized as Counts, 147
5.3.7 Ordered Categories Are Commonly Used for Severity or Toxicity, 147
5.3.8 Unordered Categories Are Sometimes Used, 148
5.3.9 Dichotomies Are Simple Summaries, 148
5.3.10 Measures of Risk, 149
5.3.11 Primary and Others, 153
5.3.12 Composites, 154
5.3.13 Event Times and Censoring, 155
5.3.14 Longitudinal Measures, 160
5.3.15 Central Review, 161
5.3.16 Patient Reported Outcomes, 161
5.4 Surrogate Outcomes, 162
5.4.1 Surrogate Outcomes Are Disease-Specific, 164
5.4.2 Surrogate Outcomes Can Make Trials More Efficient, 167
5.4.3 Surrogate Outcomes Have Significant Limitations, 168
5.5 Summary, 170
5.6 Questions for Discussion, 171
6 Random Error and Bias 172
6.1 Introduction, 172
6.1.1 The Effects of Random and Systematic Errors Are Distinct, 173
6.1.2 Hypothesis Tests versus Significance Tests, 174
6.1.3 Hypothesis Tests Are Subject to Two Types of Random Error, 175
6.1.4 Type I Errors Are Relatively Easy to Control, 176
6.1.5 The Properties of Confidence IntervalsAre Similar toHypothesis Tests, 176
6.1.6 Using a one- or two-sided hypothesis test is not the right question, 177
6.1.7 P-Values Quantify the Type I Error, 178
6.1.8 Type II Errors Depend on the Clinical Difference of Interest, 178
6.1.9 Post Hoc Power Calculations Are Useless, 180
6.2 Clinical Bias, 181
6.2.1 Relative Size of Random Error and Bias is Important, 182
6.2.2 Bias Arises from Numerous Sources, 182
6.2.3 Controlling Structural Bias is Conceptually Simple, 185
6.3 Statistical Bias, 188
6.3.1 Selection Bias, 188
6.3.2 Some Statistical Bias Can Be Corrected, 192
6.3.3 Unbiasedness is Not the Only Desirable Attribute of an Estimator, 192
6.4 Summary, 194
6.5 Questions for Discussion, 194
7 Statistical Perspectives 196
7.1 Introduction, 196
7.2 Differences in Statistical Perspectives, 197
7.2.1 Models and Parameters, 197
7.2.2 Philosophy of Inference Divides Statisticians, 198
7.2.3 Resolution, 199
7.2.4 Points of Agreement, 199
7.3 Frequentist, 202
7.3.1 Binomial Case Study, 203
7.3.2 Other Issues, 204
7.4 Bayesian, 204
7.4.1 Choice of a Prior Distribution Is a Source of Contention, 205
7.4.2 Binomial Case Study, 206
7.4.3 Bayesian Inference Is Different, 209
7.5 Likelihood, 210
7.5.1 Binomial Case Study, 211
7.5.2 Likelihood-Based Design, 211
7.6 Statistics Issues, 212
7.6.1 Perspective, 212
7.6.2 Statistical Procedures Are Not Standardized, 213
7.6.3 Practical Controversies Related to Statistics Exist, 214
7.7 Summary, 215
7.8 Questions for Discussion, 216
8 Experiment Design in Clinical Trials 217
8.1 Introduction, 217
8.2 Trials As Simple Experiment Designs, 218
8.2.1 Design Space Is Chaotic, 219
8.2.2 Design Is Critical for Inference, 220
8.2.3 The Question Drives the Design, 220
8.2.4 Design Depends on the Observation Model As Well As the
Biological Question, 221
8.2.5 Comparing Designs, 222
8.3 Goals of Experiment Design, 223
8.3.1 Control of Random Error and Bias Is the Goal, 223
8.3.2 Conceptual Simplicity Is Also a Goal, 223
8.3.3 Encapsulation of Subjectivity, 224
8.3.4 Leech Case Study, 225
8.4 Design Concepts, 225
8.4.1 The Foundations of Design Are Observation and Theory, 226
8.4.2 A Lesson from the Women's Health Initiative, 227
8.4.3 Experiments Use Three Components of Design, 229
8.5 Design Features, 230
8.5.1 Enrichment, 231
8.5.2 Replication, 232
8.5.3 Experimental and Observational Units, 232
8.5.4 Treatments and Factors, 233
8.5.5 Nesting, 233
8.5.6 Randomization, 234
8.5.7 Blocking, 234
8.5.8 Stratification, 235
8.5.9 Masking, 236
8.6 Special Design Issues, 237
8.6.1 Placebos, 237
8.6.2 Equivalence and Noninferiority, 240
8.6.3 Randomized Discontinuation, 241
8.6.4 Hybrid Designs May Be Needed for Resolving Special Questions, 242
8.6.5 Clinical Trials Cannot Meet Certain Objectives, 242
8.7 Importance of the Protocol Document, 244
8.7.1 Protocols Have Many Functions, 244
8.7.2 Deviations from Protocol Specifications are Common, 245
8.7.3 Protocols Are Structured, Logical, and Complete, 246
8.8 Summary, 252
8.9 Questions for Discussion, 253
9 The Trial Cohort 254
9.1 Introduction, 254
9.2 Cohort Definition and Selection, 255
9.2.1 Eligibility and Exclusions, 255
9.2.2 Active Sampling and Enrichment, 257
9.2.3 Participation may select subjects with better prognosis, 258
9.2.4 Quantitative Selection Criteria Versus False Precision, 262
9.2.5 Comparative Trials Are Not Sensitive to Selection, 263
9.3...
Details
| Erscheinungsjahr: | 2017 |
|---|---|
| Fachbereich: | Pharmazie |
| Genre: | Medizin |
| Rubrik: | Wissenschaften |
| Medium: | Buch |
| Seiten: | 928 |
| ISBN-13: | 9781118959206 |
| ISBN-10: | 1118959205 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Piantadosi, Steven |
| Hersteller: |
John Wiley & Sons
John Wiley & Sons Inc |
| Maße: | 262 x 189 x 53 mm |
| Von/Mit: | Steven Piantadosi |
| Erscheinungsdatum: | 27.10.2017 |
| Gewicht: | 1,803 kg |
Über den Autor
Steven Piantadosi, MD, PhD, is the Phase One Foundation Distinguished Chair and Director of the Samuel Oschin Cancer Institute, and Professor of Medicine at Cedars-Sinai Medical Center in Los Angeles, California. Dr. Piantadosi is one of the world's leading experts in the design and analysis of clinical trials for cancer research. He has taught clinical trial methods extensively in formal courses and short venues. He has advised numerous academic programs and collaborations nationally regarding clinical trial design and conduct, and has served on external advisory boards for the National Institutes of Health and other prominent cancer programs and centers. The author of more than 260 peer-reviewed scientific articles, Dr. Piantadosi has published extensively on research results, clinical applications, and trial methodology. While his papers have contributed to many areas of oncology, he has also collaborated on diverse studies outside oncology including lung disease and degenerative neurological disease.
Inhaltsverzeichnis
Preface to the Third Edition xxv
About the Companion Website xxviii
1 Preliminaries 1
1.1 Introduction, 1
1.2 Audiences, 2
1.3 Scope, 3
1.4 Other Sources of Knowledge, 5
1.5 Notation and Terminology, 6
1.5.1 Clinical Trial Terminology, 7
1.5.2 Drug Development Traditionally Recognizes Four Trial Design Types, 7
1.5.3 Descriptive Terminology Is Better, 8
1.6 Examples, Data, and Programs, 9
1.7 Summary, 9
2 Clinical Trials as Research 10
2.1 Introduction, 10
2.2 Research, 13
2.2.1 What Is Research?, 13
2.2.2 Clinical Reasoning Is Based on the Case History, 14
2.2.3 Statistical Reasoning Emphasizes Inference Based on Designed Data Production, 16
2.2.4 Clinical and Statistical Reasoning Converge in Research, 17
2.3 Defining Clinical Trials, 19
2.3.1 Mixing of Clinical and Statistical Reasoning Is Recent, 19
2.3.2 Clinical Trials Are Rigorously Defined, 21
2.3.3 Theory and Data, 22
2.3.4 Experiments Can Be Misunderstood, 23
2.3.5 Clinical Trials and the Frankenstein Myth, 25
2.3.6 Cavia porcellus, 26
2.3.7 Clinical Trials as Science, 26
2.3.8 Trials and Statistical Methods Fit within a Spectrum of Clinical Research, 28
2.4 Practicalities of Usage, 29
2.4.1 Predicates for a Trial, 29
2.4.2 Trials Can Provide Confirmatory Evidence, 29
2.4.3 Clinical Trials Are Reliable Albeit Unwieldy and Messy, 30
2.4.4 Trials Are Difficult to Apply in Some Circumstances, 31
2.4.5 Randomized Studies Can Be Initiated Early, 32
2.4.6 What Can I learn from = 20?, 33
2.5 Nonexperimental Designs, 35
2.5.1 Other Methods Are Valid forMaking Some Clinical Inferences, 35
2.5.2 Some Specific Nonexperimental Designs, 38
2.5.3 Causal Relationships, 40
2.5.4 Will Genetic Determinism Replace Design?, 41
2.6 Summary, 41
2.7 Questions for Discussion, 41
3 Why Clinical Trials Are Ethical 43
3.1 Introduction, 43
3.1.1 Science and Ethics Share Objectives, 44
3.1.2 Equipoise and Uncertainty, 46
3.2 Duality, 47
3.2.1 Clinical Trials Sharpen, But Do Not Create, Duality, 47
3.2.2 A Gene Therapy Tragedy Illustrates Duality, 48
3.2.3 Research and Practice Are Convergent, 48
3.2.4 Hippocratic Tradition Does Not Proscribe Clinical Trials, 52
3.2.5 Physicians Always Have Multiple Roles, 54
3.3 Historically Derived Principles of Ethics, 57
3.3.1 Nuremberg Contributed an Awareness of the Worst Problems, 57
3.3.2 High-Profile Mistakes Were Made in the United States, 58
3.3.3 The Helsinki Declaration Was Widely Adopted, 58
3.3.4 Other International Guidelines Have Been Proposed, 61
3.3.5 Institutional Review Boards Provide Ethics Oversight, 62
3.3.6 Ethics Principles Relevant to Clinical Trials, 63
3.4 Contemporary Foundational Principles, 65
3.4.1 Collaborative Partnership, 66
3.4.2 Scientific Value, 66
3.4.3 Scientific Validity, 66
3.4.4 Fair Subject Selection, 67
3.4.5 Favorable Risk-Benefit, 67
3.4.6 Independent Review, 68
3.4.7 Informed Consent, 68
3.4.8 Respect for Subjects, 71
3.5 Methodologic Reflections, 72
3.5.1 Practice Based on Unproven Treatments Is Not Ethical, 72
3.5.2 Ethics Considerations Are Important Determinants of Design, 74
3.5.3 Specific Methods Have Justification, 75
3.6 Professional Conduct, 79
3.6.1 Advocacy, 79
3.6.2 Physician to Physician Communication Is Not Research, 81
3.6.3 Investigator Responsibilities, 82
3.6.4 Professional Ethics, 83
3.7 Summary, 85
3.8 Questions for Discussion, 86
4 Contexts for Clinical Trials 87
4.1 Introduction, 87
4.1.1 Clinical Trial Registries, 88
4.1.2 Public Perception Versus Science, 90
4.2 Drugs, 91
4.2.1 Are Drugs Special?, 92
4.2.2 Why Trials Are Used Extensively for Drugs, 93
4.3 Devices, 95
4.3.1 Use of Trials for Medical Devices, 95
4.3.2 Are Devices Different from Drugs?, 97
4.3.3 Case Study, 98
4.4 Prevention, 99
4.4.1 The Prevention versus Therapy Dichotomy Is Over-worked, 100
4.4.2 Vaccines and Biologicals, 101
4.4.3 Ebola 2014 and Beyond, 102
4.4.4 A Perspective on Risk-Benefit, 103
4.4.5 Methodology and Framework for Prevention Trials, 105
4.5 Complementary and Alternative Medicine, 106
4.5.1 Science Is the Study of Natural Phenomena, 108
4.5.2 Ignorance Is Important, 109
4.5.3 The Essential Paradox of CAM and Clinical Trials, 110
4.5.4 Why Trials Have Not Been Used Extensively in CAM, 111
4.5.5 Some Principles for Rigorous Evaluation, 113
4.5.6 Historic Examples, 115
4.6 Surgery and Skill-Dependent Therapies, 116
4.6.1 Why Trials Have Been Used Less Extensively in Surgery, 118
4.6.2 Reasons Why Some Surgical Therapies Require Less Rigorous Study Designs, 120
4.6.3 Sources of Variation, 121
4.6.4 Difficulties of Inference, 121
4.6.5 Control of Observer Bias Is Possible, 122
4.6.6 Illustrations from an Emphysema Surgery Trial, 124
4.7 A Brief View of Some Other Contexts, 130
4.7.1 Screening Trials, 130
4.7.2 Diagnostic Trials, 134
4.7.3 Radiation Therapy, 134
4.8 Summary, 135
4.9 Questions for Discussion, 136
5 Measurement 137
5.1 Introduction, 137
5.1.1 Types of Uncertainty, 138
5.2 Objectives, 140
5.2.1 Estimation Is The Most Common Objective, 141
5.2.2 Selection Can Also Be an Objective, 141
5.2.3 Objectives Require Various Scales of Measurement, 142
5.3 Measurement Design, 143
5.3.1 Mixed Outcomes and Predictors, 143
5.3.2 Criteria for Evaluating Outcomes, 144
5.3.3 Prefer Hard or Objective Outcomes, 145
5.3.4 Outcomes Can Be Quantitative or Qualitative, 146
5.3.5 Measures Are Useful and Efficient Outcomes, 146
5.3.6 Some Outcomes Are Summarized as Counts, 147
5.3.7 Ordered Categories Are Commonly Used for Severity or Toxicity, 147
5.3.8 Unordered Categories Are Sometimes Used, 148
5.3.9 Dichotomies Are Simple Summaries, 148
5.3.10 Measures of Risk, 149
5.3.11 Primary and Others, 153
5.3.12 Composites, 154
5.3.13 Event Times and Censoring, 155
5.3.14 Longitudinal Measures, 160
5.3.15 Central Review, 161
5.3.16 Patient Reported Outcomes, 161
5.4 Surrogate Outcomes, 162
5.4.1 Surrogate Outcomes Are Disease-Specific, 164
5.4.2 Surrogate Outcomes Can Make Trials More Efficient, 167
5.4.3 Surrogate Outcomes Have Significant Limitations, 168
5.5 Summary, 170
5.6 Questions for Discussion, 171
6 Random Error and Bias 172
6.1 Introduction, 172
6.1.1 The Effects of Random and Systematic Errors Are Distinct, 173
6.1.2 Hypothesis Tests versus Significance Tests, 174
6.1.3 Hypothesis Tests Are Subject to Two Types of Random Error, 175
6.1.4 Type I Errors Are Relatively Easy to Control, 176
6.1.5 The Properties of Confidence IntervalsAre Similar toHypothesis Tests, 176
6.1.6 Using a one- or two-sided hypothesis test is not the right question, 177
6.1.7 P-Values Quantify the Type I Error, 178
6.1.8 Type II Errors Depend on the Clinical Difference of Interest, 178
6.1.9 Post Hoc Power Calculations Are Useless, 180
6.2 Clinical Bias, 181
6.2.1 Relative Size of Random Error and Bias is Important, 182
6.2.2 Bias Arises from Numerous Sources, 182
6.2.3 Controlling Structural Bias is Conceptually Simple, 185
6.3 Statistical Bias, 188
6.3.1 Selection Bias, 188
6.3.2 Some Statistical Bias Can Be Corrected, 192
6.3.3 Unbiasedness is Not the Only Desirable Attribute of an Estimator, 192
6.4 Summary, 194
6.5 Questions for Discussion, 194
7 Statistical Perspectives 196
7.1 Introduction, 196
7.2 Differences in Statistical Perspectives, 197
7.2.1 Models and Parameters, 197
7.2.2 Philosophy of Inference Divides Statisticians, 198
7.2.3 Resolution, 199
7.2.4 Points of Agreement, 199
7.3 Frequentist, 202
7.3.1 Binomial Case Study, 203
7.3.2 Other Issues, 204
7.4 Bayesian, 204
7.4.1 Choice of a Prior Distribution Is a Source of Contention, 205
7.4.2 Binomial Case Study, 206
7.4.3 Bayesian Inference Is Different, 209
7.5 Likelihood, 210
7.5.1 Binomial Case Study, 211
7.5.2 Likelihood-Based Design, 211
7.6 Statistics Issues, 212
7.6.1 Perspective, 212
7.6.2 Statistical Procedures Are Not Standardized, 213
7.6.3 Practical Controversies Related to Statistics Exist, 214
7.7 Summary, 215
7.8 Questions for Discussion, 216
8 Experiment Design in Clinical Trials 217
8.1 Introduction, 217
8.2 Trials As Simple Experiment Designs, 218
8.2.1 Design Space Is Chaotic, 219
8.2.2 Design Is Critical for Inference, 220
8.2.3 The Question Drives the Design, 220
8.2.4 Design Depends on the Observation Model As Well As the
Biological Question, 221
8.2.5 Comparing Designs, 222
8.3 Goals of Experiment Design, 223
8.3.1 Control of Random Error and Bias Is the Goal, 223
8.3.2 Conceptual Simplicity Is Also a Goal, 223
8.3.3 Encapsulation of Subjectivity, 224
8.3.4 Leech Case Study, 225
8.4 Design Concepts, 225
8.4.1 The Foundations of Design Are Observation and Theory, 226
8.4.2 A Lesson from the Women's Health Initiative, 227
8.4.3 Experiments Use Three Components of Design, 229
8.5 Design Features, 230
8.5.1 Enrichment, 231
8.5.2 Replication, 232
8.5.3 Experimental and Observational Units, 232
8.5.4 Treatments and Factors, 233
8.5.5 Nesting, 233
8.5.6 Randomization, 234
8.5.7 Blocking, 234
8.5.8 Stratification, 235
8.5.9 Masking, 236
8.6 Special Design Issues, 237
8.6.1 Placebos, 237
8.6.2 Equivalence and Noninferiority, 240
8.6.3 Randomized Discontinuation, 241
8.6.4 Hybrid Designs May Be Needed for Resolving Special Questions, 242
8.6.5 Clinical Trials Cannot Meet Certain Objectives, 242
8.7 Importance of the Protocol Document, 244
8.7.1 Protocols Have Many Functions, 244
8.7.2 Deviations from Protocol Specifications are Common, 245
8.7.3 Protocols Are Structured, Logical, and Complete, 246
8.8 Summary, 252
8.9 Questions for Discussion, 253
9 The Trial Cohort 254
9.1 Introduction, 254
9.2 Cohort Definition and Selection, 255
9.2.1 Eligibility and Exclusions, 255
9.2.2 Active Sampling and Enrichment, 257
9.2.3 Participation may select subjects with better prognosis, 258
9.2.4 Quantitative Selection Criteria Versus False Precision, 262
9.2.5 Comparative Trials Are Not Sensitive to Selection, 263
9.3...
About the Companion Website xxviii
1 Preliminaries 1
1.1 Introduction, 1
1.2 Audiences, 2
1.3 Scope, 3
1.4 Other Sources of Knowledge, 5
1.5 Notation and Terminology, 6
1.5.1 Clinical Trial Terminology, 7
1.5.2 Drug Development Traditionally Recognizes Four Trial Design Types, 7
1.5.3 Descriptive Terminology Is Better, 8
1.6 Examples, Data, and Programs, 9
1.7 Summary, 9
2 Clinical Trials as Research 10
2.1 Introduction, 10
2.2 Research, 13
2.2.1 What Is Research?, 13
2.2.2 Clinical Reasoning Is Based on the Case History, 14
2.2.3 Statistical Reasoning Emphasizes Inference Based on Designed Data Production, 16
2.2.4 Clinical and Statistical Reasoning Converge in Research, 17
2.3 Defining Clinical Trials, 19
2.3.1 Mixing of Clinical and Statistical Reasoning Is Recent, 19
2.3.2 Clinical Trials Are Rigorously Defined, 21
2.3.3 Theory and Data, 22
2.3.4 Experiments Can Be Misunderstood, 23
2.3.5 Clinical Trials and the Frankenstein Myth, 25
2.3.6 Cavia porcellus, 26
2.3.7 Clinical Trials as Science, 26
2.3.8 Trials and Statistical Methods Fit within a Spectrum of Clinical Research, 28
2.4 Practicalities of Usage, 29
2.4.1 Predicates for a Trial, 29
2.4.2 Trials Can Provide Confirmatory Evidence, 29
2.4.3 Clinical Trials Are Reliable Albeit Unwieldy and Messy, 30
2.4.4 Trials Are Difficult to Apply in Some Circumstances, 31
2.4.5 Randomized Studies Can Be Initiated Early, 32
2.4.6 What Can I learn from = 20?, 33
2.5 Nonexperimental Designs, 35
2.5.1 Other Methods Are Valid forMaking Some Clinical Inferences, 35
2.5.2 Some Specific Nonexperimental Designs, 38
2.5.3 Causal Relationships, 40
2.5.4 Will Genetic Determinism Replace Design?, 41
2.6 Summary, 41
2.7 Questions for Discussion, 41
3 Why Clinical Trials Are Ethical 43
3.1 Introduction, 43
3.1.1 Science and Ethics Share Objectives, 44
3.1.2 Equipoise and Uncertainty, 46
3.2 Duality, 47
3.2.1 Clinical Trials Sharpen, But Do Not Create, Duality, 47
3.2.2 A Gene Therapy Tragedy Illustrates Duality, 48
3.2.3 Research and Practice Are Convergent, 48
3.2.4 Hippocratic Tradition Does Not Proscribe Clinical Trials, 52
3.2.5 Physicians Always Have Multiple Roles, 54
3.3 Historically Derived Principles of Ethics, 57
3.3.1 Nuremberg Contributed an Awareness of the Worst Problems, 57
3.3.2 High-Profile Mistakes Were Made in the United States, 58
3.3.3 The Helsinki Declaration Was Widely Adopted, 58
3.3.4 Other International Guidelines Have Been Proposed, 61
3.3.5 Institutional Review Boards Provide Ethics Oversight, 62
3.3.6 Ethics Principles Relevant to Clinical Trials, 63
3.4 Contemporary Foundational Principles, 65
3.4.1 Collaborative Partnership, 66
3.4.2 Scientific Value, 66
3.4.3 Scientific Validity, 66
3.4.4 Fair Subject Selection, 67
3.4.5 Favorable Risk-Benefit, 67
3.4.6 Independent Review, 68
3.4.7 Informed Consent, 68
3.4.8 Respect for Subjects, 71
3.5 Methodologic Reflections, 72
3.5.1 Practice Based on Unproven Treatments Is Not Ethical, 72
3.5.2 Ethics Considerations Are Important Determinants of Design, 74
3.5.3 Specific Methods Have Justification, 75
3.6 Professional Conduct, 79
3.6.1 Advocacy, 79
3.6.2 Physician to Physician Communication Is Not Research, 81
3.6.3 Investigator Responsibilities, 82
3.6.4 Professional Ethics, 83
3.7 Summary, 85
3.8 Questions for Discussion, 86
4 Contexts for Clinical Trials 87
4.1 Introduction, 87
4.1.1 Clinical Trial Registries, 88
4.1.2 Public Perception Versus Science, 90
4.2 Drugs, 91
4.2.1 Are Drugs Special?, 92
4.2.2 Why Trials Are Used Extensively for Drugs, 93
4.3 Devices, 95
4.3.1 Use of Trials for Medical Devices, 95
4.3.2 Are Devices Different from Drugs?, 97
4.3.3 Case Study, 98
4.4 Prevention, 99
4.4.1 The Prevention versus Therapy Dichotomy Is Over-worked, 100
4.4.2 Vaccines and Biologicals, 101
4.4.3 Ebola 2014 and Beyond, 102
4.4.4 A Perspective on Risk-Benefit, 103
4.4.5 Methodology and Framework for Prevention Trials, 105
4.5 Complementary and Alternative Medicine, 106
4.5.1 Science Is the Study of Natural Phenomena, 108
4.5.2 Ignorance Is Important, 109
4.5.3 The Essential Paradox of CAM and Clinical Trials, 110
4.5.4 Why Trials Have Not Been Used Extensively in CAM, 111
4.5.5 Some Principles for Rigorous Evaluation, 113
4.5.6 Historic Examples, 115
4.6 Surgery and Skill-Dependent Therapies, 116
4.6.1 Why Trials Have Been Used Less Extensively in Surgery, 118
4.6.2 Reasons Why Some Surgical Therapies Require Less Rigorous Study Designs, 120
4.6.3 Sources of Variation, 121
4.6.4 Difficulties of Inference, 121
4.6.5 Control of Observer Bias Is Possible, 122
4.6.6 Illustrations from an Emphysema Surgery Trial, 124
4.7 A Brief View of Some Other Contexts, 130
4.7.1 Screening Trials, 130
4.7.2 Diagnostic Trials, 134
4.7.3 Radiation Therapy, 134
4.8 Summary, 135
4.9 Questions for Discussion, 136
5 Measurement 137
5.1 Introduction, 137
5.1.1 Types of Uncertainty, 138
5.2 Objectives, 140
5.2.1 Estimation Is The Most Common Objective, 141
5.2.2 Selection Can Also Be an Objective, 141
5.2.3 Objectives Require Various Scales of Measurement, 142
5.3 Measurement Design, 143
5.3.1 Mixed Outcomes and Predictors, 143
5.3.2 Criteria for Evaluating Outcomes, 144
5.3.3 Prefer Hard or Objective Outcomes, 145
5.3.4 Outcomes Can Be Quantitative or Qualitative, 146
5.3.5 Measures Are Useful and Efficient Outcomes, 146
5.3.6 Some Outcomes Are Summarized as Counts, 147
5.3.7 Ordered Categories Are Commonly Used for Severity or Toxicity, 147
5.3.8 Unordered Categories Are Sometimes Used, 148
5.3.9 Dichotomies Are Simple Summaries, 148
5.3.10 Measures of Risk, 149
5.3.11 Primary and Others, 153
5.3.12 Composites, 154
5.3.13 Event Times and Censoring, 155
5.3.14 Longitudinal Measures, 160
5.3.15 Central Review, 161
5.3.16 Patient Reported Outcomes, 161
5.4 Surrogate Outcomes, 162
5.4.1 Surrogate Outcomes Are Disease-Specific, 164
5.4.2 Surrogate Outcomes Can Make Trials More Efficient, 167
5.4.3 Surrogate Outcomes Have Significant Limitations, 168
5.5 Summary, 170
5.6 Questions for Discussion, 171
6 Random Error and Bias 172
6.1 Introduction, 172
6.1.1 The Effects of Random and Systematic Errors Are Distinct, 173
6.1.2 Hypothesis Tests versus Significance Tests, 174
6.1.3 Hypothesis Tests Are Subject to Two Types of Random Error, 175
6.1.4 Type I Errors Are Relatively Easy to Control, 176
6.1.5 The Properties of Confidence IntervalsAre Similar toHypothesis Tests, 176
6.1.6 Using a one- or two-sided hypothesis test is not the right question, 177
6.1.7 P-Values Quantify the Type I Error, 178
6.1.8 Type II Errors Depend on the Clinical Difference of Interest, 178
6.1.9 Post Hoc Power Calculations Are Useless, 180
6.2 Clinical Bias, 181
6.2.1 Relative Size of Random Error and Bias is Important, 182
6.2.2 Bias Arises from Numerous Sources, 182
6.2.3 Controlling Structural Bias is Conceptually Simple, 185
6.3 Statistical Bias, 188
6.3.1 Selection Bias, 188
6.3.2 Some Statistical Bias Can Be Corrected, 192
6.3.3 Unbiasedness is Not the Only Desirable Attribute of an Estimator, 192
6.4 Summary, 194
6.5 Questions for Discussion, 194
7 Statistical Perspectives 196
7.1 Introduction, 196
7.2 Differences in Statistical Perspectives, 197
7.2.1 Models and Parameters, 197
7.2.2 Philosophy of Inference Divides Statisticians, 198
7.2.3 Resolution, 199
7.2.4 Points of Agreement, 199
7.3 Frequentist, 202
7.3.1 Binomial Case Study, 203
7.3.2 Other Issues, 204
7.4 Bayesian, 204
7.4.1 Choice of a Prior Distribution Is a Source of Contention, 205
7.4.2 Binomial Case Study, 206
7.4.3 Bayesian Inference Is Different, 209
7.5 Likelihood, 210
7.5.1 Binomial Case Study, 211
7.5.2 Likelihood-Based Design, 211
7.6 Statistics Issues, 212
7.6.1 Perspective, 212
7.6.2 Statistical Procedures Are Not Standardized, 213
7.6.3 Practical Controversies Related to Statistics Exist, 214
7.7 Summary, 215
7.8 Questions for Discussion, 216
8 Experiment Design in Clinical Trials 217
8.1 Introduction, 217
8.2 Trials As Simple Experiment Designs, 218
8.2.1 Design Space Is Chaotic, 219
8.2.2 Design Is Critical for Inference, 220
8.2.3 The Question Drives the Design, 220
8.2.4 Design Depends on the Observation Model As Well As the
Biological Question, 221
8.2.5 Comparing Designs, 222
8.3 Goals of Experiment Design, 223
8.3.1 Control of Random Error and Bias Is the Goal, 223
8.3.2 Conceptual Simplicity Is Also a Goal, 223
8.3.3 Encapsulation of Subjectivity, 224
8.3.4 Leech Case Study, 225
8.4 Design Concepts, 225
8.4.1 The Foundations of Design Are Observation and Theory, 226
8.4.2 A Lesson from the Women's Health Initiative, 227
8.4.3 Experiments Use Three Components of Design, 229
8.5 Design Features, 230
8.5.1 Enrichment, 231
8.5.2 Replication, 232
8.5.3 Experimental and Observational Units, 232
8.5.4 Treatments and Factors, 233
8.5.5 Nesting, 233
8.5.6 Randomization, 234
8.5.7 Blocking, 234
8.5.8 Stratification, 235
8.5.9 Masking, 236
8.6 Special Design Issues, 237
8.6.1 Placebos, 237
8.6.2 Equivalence and Noninferiority, 240
8.6.3 Randomized Discontinuation, 241
8.6.4 Hybrid Designs May Be Needed for Resolving Special Questions, 242
8.6.5 Clinical Trials Cannot Meet Certain Objectives, 242
8.7 Importance of the Protocol Document, 244
8.7.1 Protocols Have Many Functions, 244
8.7.2 Deviations from Protocol Specifications are Common, 245
8.7.3 Protocols Are Structured, Logical, and Complete, 246
8.8 Summary, 252
8.9 Questions for Discussion, 253
9 The Trial Cohort 254
9.1 Introduction, 254
9.2 Cohort Definition and Selection, 255
9.2.1 Eligibility and Exclusions, 255
9.2.2 Active Sampling and Enrichment, 257
9.2.3 Participation may select subjects with better prognosis, 258
9.2.4 Quantitative Selection Criteria Versus False Precision, 262
9.2.5 Comparative Trials Are Not Sensitive to Selection, 263
9.3...
Details
| Erscheinungsjahr: | 2017 |
|---|---|
| Fachbereich: | Pharmazie |
| Genre: | Medizin |
| Rubrik: | Wissenschaften |
| Medium: | Buch |
| Seiten: | 928 |
| ISBN-13: | 9781118959206 |
| ISBN-10: | 1118959205 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Piantadosi, Steven |
| Hersteller: |
John Wiley & Sons
John Wiley & Sons Inc |
| Maße: | 262 x 189 x 53 mm |
| Von/Mit: | Steven Piantadosi |
| Erscheinungsdatum: | 27.10.2017 |
| Gewicht: | 1,803 kg |
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