Multiple analyses in clinical trials fundamentals for investigators

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1. Verfasser: Moyé, Lemuel A. 1952- (VerfasserIn)
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Sprache:English
Veröffentlicht: New York ; Berlin ; Heidelberg ; Hong Kong ; London ; Milan Springer 2003
Schriftenreihe:Statistics for biology and health
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Datensatz im Suchindex

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adam_text Contents Blossoms on a Healthy Plant 1 Epidemiology and Biostatistics 1 Association and Causation 2 The Central Core of Epidemiology 3 Scurvy 4 Cholera 5 Causality and Common Sense 6 Biostatisticians and Sampling Error 7 Cooperation Between the Disciplines 7 The Eye of the Beholder 10 Controversy 1: Biased by Nature? 10 The Supremacy of Mathematics? 12 Hammer Blows 13 The Rise and Decline of Significance Testing 14 Advice to the Physician Scientist 17 References 18 Chapter 1. Fundamentals of Clinical Trial Design 21 1.1 The Definition of a Clinical Trial 21 1.2 Principles of Randomization 22 1.2.1 Random Selection of Subjects from the Population 22 1.2.2 Random Allocation of Therapy 23 1.2.3 Stratified Randomization 25 1.3 The Use of Blinding 25 1.3.1 Single Blinded Trials 26 1.3.2 Double Blind Studies 27 1.3.3 Arthroscopy and the Art of the Double Blind 28 1.4 Interim Monitoring of Clinical Trials 30 xl 1.4.1 The Need for Trial Monitoring 30 1.4.2 Test Statistic Trajectories 31 1.4.3 Group Sequential Procedures 37 1.4.4 Stochastic Curtailment 39 1.5 Intention to Treat Analyses 39 1.6 Measures of Effect 40 1.7 The Goal of Statistical Hypothesis Testing 41 1.8 Sampling Error and Significance Testing 42 1.9 Statistical Power 43 1.10 Sample Size Computations 44 1.11 Analysis 44 References 45 Chapter 2. Multiple Analyses and the Random Experiment 47 2.1 Introduction 49 2.1.1 Advisory Committee Discussions 50 2.2 Prevalent Perceptions 50 2.3 Calling Your Shot 51 2.3.1 Vesnarinone 52 2.3.2 Losartan 54 2.3.3 Amlodipine 55 2.3.4 Carvedilol 56 2.3.5 Experimental Inconsistencies 58 2.4 Samples as Reflections 59 2.5 Representative Samples That Mislead? 60 2.6 Estimators 63 2.7 Random Experiments 64 2.7.1 What Is a Random Protocol? 64 2.7.2 Crippled Estimators in Random Experiments 65 2.7.3 Untrustworthy 65 2.8 Collecting the Entire Population 66 2.9 Regulatory Example 67 2.10 Additional Comments 68 2.10.1 Don t Rush to Judgment 68 2.10.2 Random Research in Extremis 69 2.10.3 Requirements of Investigators and Readers 69 2.11 Conclusions 70 Problems 70 References 71 Chapter 3. The Lure and Complexity of Multiple Analyses 75 3.1 Definition of Multiple Analyses 75 3.2 Why Do Multiple Analyses? 76 3.2.1 Logistical Efficiency 77 3.2.2 Epidemiologic Strength 77 3.2.3 The Need to Explore 79 3.3 Hypothesis Testing in Multiple Analyses 80 3.3.1 Nominal P Values 80 3.3.2 The Error of Interest: Familywise Error 81 3.3.3 Initial Computations for£ 82 3.3.4 FDA and Strength of Evidence 84 3.4 Is Tight Control of the FWER Necessary? 85 3.5 Community Protection 86 3.6 Efficacy and Drug Labels 89 3.7 The Bonferroni Inequality 90 3.8 Who Was Bonferroni? 93 3.9 Alternative Approaches 94 3.9.1 Sequentially Rejective Procedures 94 3.9.2 Who Chooses the a Level Threshold? 95 3.9.3 Resampling P Values 98 3.10 Conclusions 99 Problems 100 References 101 Chapter 4. Multiple Analyses and Multiple Endpoints 105 4.1 Introduction 105 4.2 Important Assumptions 106 4.3 Clinical Trial Result Descriptors 106 4.3.1 Positive and Negative Trials 106 4.3.2 Null Results Versus Uninformative Results 107 4.4 The Strategy for Multiple Endpoint Analysis 108 4.5 Tactic 1: Triage the Endpoints 110 4.5.1 The Process of Endpoint Triage Ill 4.5.2 An Example of the Endpoint Triage Process Ill 4.5.3 Other Motivations for Triaging Endpoints 112 4.5.4 Endpoint Triaging and Labeling Indications 113 4.6 Endpoint Descriptors 113 4.6.1 Primary Endpoints 114 4.6.2 Secondary Endpoints 115 4.6.3 Exploratory Endpoints 116 4.6.4 Choose Wisely 119 4.6.5 Planning Well to Learn Well 119 4.7 Mapping Out the Manuscript 121 4.7.1 The Design Manuscript 121 4.7.2 Laying Out the Manuscripts 122 4.8 Multiple Primary Endpoint Interpretations 124 4.9 Tactic 2: Differential a Allocation 126 4.9.1 Differential a Rate Allocation 127 4.9.2 Clinical Decisions in Allocating a 130 4.9.3 Example 1: Different Community Standards 131 4.9.4 Example 2: The Underpowered Environment 135 4.9.5 Example 3: Efficacy Reconsideration 139 4.9.6 Example 4: Multiple Endpoints 143 4.10 Multiple Analyses 145 4.10.1 Example 146 4.11 Theory Versus Reality 148 Problems 152 References 153 Chapter 5. Introduction to Multiple Dependent Analyses 1 155 5.1 Rationale for Dependent Testing 155 5.1.1 Review 155 5.2 The Notion of Dependent Analyses 156 5.2.1 The Nature of Relationships 156 5.2.2 Endpoint Coincidence and Correlation 158 5.2.3 Surrogate Endpoint Definition 162 5.3 Literature Review 163 5.3.1 Tukey s Procedure and Related Ad Hoc Computations 163 5.4 Hypothesis Test Dependency: Notation 166 5.5 The Independence Scenario 167 5.6 Demonstration of Perfect Dependence 169 5.7 Scenario Contrasts 171 5.8 Creation of the Dependency Parameter 172 5.9 Solving for a2 as a Function of D 176 5.10 Example 1: Implantable Cardiac Devices 177 5.11 Example 2: The CURE Trial 181 5.12 Example 3: Paroxysmal Atrial Fibrillation 183 5.13 Choosing the Dependency Parameter 187 5.13.1 Overestimation of Dependency Parameter 187 5.13.2 Guides to Choosing the Dependency Parameter 188 5.14 Hyperdependent Analyses 191 5.14.1 Hyperdependence as a Disadvantage 191 5.14.2 Hyperdependence as an Advantage 193 Problems 195 References 196 Chapter 6. Multiple Dependent Analyses II 199 6.1 Three Multidependent Analyses 199 6.1.1 Example of Dependency Among Three Endpoints 202 6.2 The Solution for Four Dependent Analyses 205 6.3 K Multidependent Analyses 206 6.4 Conservative Dependence 207 6.5 Generalization of the Bonferroni Inequality 209 6.6 Subclass Dependence 210 6.6.1 Solutions for Two Subclasses 210 6.6.2 Therapy for CHF 212 6.7 Conclusions 216 Problems 217 References 218 Chapter 7. Introduction to Composite Endpoints 219 7.1 Introduction 219 7.2 Definitions and Motivations 220 7.3 Notation 220 7.4 Motivations for Combined Endpoints 220 7.4.1 Epidemiologic Considerations 221 7.4.2 Sample Size Concerns 221 7.4.3 Improved Resolving Power 223 7.5 Properties of Combined Endpoints 225 7.6 Component Endpoint Coherence 225 7.7 Coincidence 225 7.8 Mutual Exclusivity and Disparate Events 227 7.9 The Problem with Mutual Exclusivity 228 7.10 Balancing the Separation 229 7.11 Component Endpoint Equivalence 230 7.12 Therapy Homogeneity 232 7.13 Composite Endpoint Measurement Rules 234 7.14 Prospective Identification 234 7.15 Combined Endpoint Ascertainment 235 7.16 Conclusions 236 Problems 237 References 237 Chapter 8. Multiple Analyses and Composite Endpoints 239 8.1 Examples of Composite Endpoint Use 239 8.2 Lipid Research Clinics 240 8.3UKPDS 241 8.4 HOPE 245 8.5 Principles of Combined Endpoint Use 247 8.6 a Allocation and Combined Endpoints 249 8.6.1 Familywise Error Control Procedures 249 8.6.2 Multiple Analyses and Composite Endpoint Distress 249 8.7 Example 1: Design for a Heart Failure Trial 251 8.8 Composite Endpoints: Diabetes Mellitus 256 8.9 Soft Components 261 8.10 Conclusions 263 Problems 264 References 264 Chapter 9. Introduction to Subgroup Analyses 267 9.1 Introduction 267 9.2 Definitions and Basic Concepts 268 9.2.1 Subgroups Versus Subgroup Strata 268 9.3 Interpretation Difficulties 269 9.4 Random Subgroups 269 9.5 Stratified Randomization 271 9.6 Proper Versus Improper Subgroups 274 9.7 Intention to Treat Versus As Treated 278 9.8 Example 1: Diabetes Mellitus in SAVE 279 9.9 Subgroup Result Depiction 281 Problems 283 References 284 Chapter 10. Subgroups II: Effect Domination and Controversy 287 10.1 Effect Domination Principle 287 10.2 Assessment of Subgroup Effects 288 10.2.1 Effect Modification and Interaction Analyses 288 10.2.2 Within Stratum Effects 290 10.3 Problematic Subgroup Analyses 291 10.4 The MERIT Trial 292 10.5 Ethnicity and ACE i therapy 295 10.6 The NETT Study 298 10.7 The Difficulties Continue 300 Problems 301 References 301 Chapter 11. Subgroups III: Confirmatory Analyses 303 11.1 Introduction 303 11.2 Focus on Stratum Specific Effects 304 11.3 Confirmatory Analyses Requisites 305 11.4 Incorporating Subgroup Dependency 305 11.4.1 Therapy Homogeneity in Subgroup Evaluations 307 11.5 Subgroup Stratum Specific Endpoints 310 11.5.1 Choosing the Subcohort Endpoints 311 11.5.2 Example 311 11.5.3 Dependency Parameter s Minimal Impact 316 11.6 Differential Event Rate 316 11.6.1 Event Rate Differences 318 11.7 Differential Efficacy 322 11.7.1 The Relationship Between Efficacy and Sample Size 322 11.7.2 Choosing an Efficacy Level 324 11.7.3 Matching Clinical and Statistical Significance 327 11.7.4 Example 328 11.8 The Differential Use of Event Precision 332 11.8.1 Sample Sizes for Continuous Endpoints 332 11.8.2 Cohort Dependent Precision 333 11.9 Conclusions 335 Problems 337 Note 337 References 339 Chapter 12. Multiple Analyses and Multiple Treatment Arms 341 12.1 Introduction and Assumptions 341 12.2 Literature Review 342 12.3 Treatment Versus Treatment 343 12.4 Dose Response Effects 350 12.5 Conclusions 356 References 357 Chapter 13. Combining Multiple Analyses 359 13.1 Introduction 359 13.2 Creating a Multiple Analysis Environment 360 13.3 Composite Endpoints Within Subgroups 363 13.4 Majority Subgroups in Clinical Trials 366 13.5 Atherosclerotic Disease Trial Designs 370 13.6 Multiple Treatment Groups Revisited 375 References 378 Chapter 14. Conclusions: The Two Front War 379 14.1 Compromise and Sampling Error 379 14.2 The Handcuffs 381 References 384 Appendix A. Case Reports and Causality 385 A.I Causality Tenets 387 A.2 Do Case Reports Prove Causality? 390 References 392 Appendix B. Estimation in Random Research 393 B.I Introduction 393 B.2 Dichotomous Clinical Events 394 B.2.1 Event Rate for the Fixed Research Paradigm 394 B.2.2 Event Rates in Random Research 397 B.3 Hypothesis Testing 401 Appendix C. Relevant Code of Federal Regulations 403 C.I Indications for Prescritpion Drugs 403 C.2 Adequate and Well Controlled Trials 404 Appendix D. Sample Size Primer 409 D.I General Discussion of Sample Size 409 D.2 Derivation of Sample Size 412 D.3 Example 414 References 415 Appendix E. Additional Dependent Hypothesis Testing Results 417 E.I Derivation of Dependence for K = 4 417 E.2 Induction Arguments 419 E.3 Additional Recursive Relationships 423 References 429 Index 431
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spellingShingle Moyé, Lemuel A. 1952-
Multiple analyses in clinical trials fundamentals for investigators
Analyse multivariée
Biologie gtt
Geneeskunde gtt
Statistische analyse gtt
Études cliniques
Études cliniques - Méthodes statistiques
Medizin
Clinical Trials as Topic methods
Clinical trials
Clinical trials Statistical methods
Multivariate Analysis
Multivariate analysis
Research Design
Multivariate Analyse (DE-588)4040708-1 gnd
Klinisches Experiment (DE-588)4164223-5 gnd
subject_GND (DE-588)4040708-1
(DE-588)4164223-5
title Multiple analyses in clinical trials fundamentals for investigators
title_auth Multiple analyses in clinical trials fundamentals for investigators
title_exact_search Multiple analyses in clinical trials fundamentals for investigators
title_full Multiple analyses in clinical trials fundamentals for investigators Lemuel A. Moyé
title_fullStr Multiple analyses in clinical trials fundamentals for investigators Lemuel A. Moyé
title_full_unstemmed Multiple analyses in clinical trials fundamentals for investigators Lemuel A. Moyé
title_short Multiple analyses in clinical trials
title_sort multiple analyses in clinical trials fundamentals for investigators
title_sub fundamentals for investigators
topic Analyse multivariée
Biologie gtt
Geneeskunde gtt
Statistische analyse gtt
Études cliniques
Études cliniques - Méthodes statistiques
Medizin
Clinical Trials as Topic methods
Clinical trials
Clinical trials Statistical methods
Multivariate Analysis
Multivariate analysis
Research Design
Multivariate Analyse (DE-588)4040708-1 gnd
Klinisches Experiment (DE-588)4164223-5 gnd
topic_facet Analyse multivariée
Biologie
Geneeskunde
Statistische analyse
Études cliniques
Études cliniques - Méthodes statistiques
Medizin
Clinical Trials as Topic methods
Clinical trials
Clinical trials Statistical methods
Multivariate Analysis
Multivariate analysis
Research Design
Multivariate Analyse
Klinisches Experiment
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