Methods and Applications of Statistics in Clinical Trials
Wiley Blackwell (Verlag)
978-1-118-30473-0 (ISBN)
This comprehensive book features both new and established material on the key statistical principles and concepts for designing modern-day clinical trials, such as hazard ratio, flexible designs, confounding, covariates, missing data, and longitudinal data. It discusses the various kinds of trials that can be found in today's clinical setting including open-labeled trials, multicentered trials, and superiority trials. It also explores such ongoing, cutting-edge trials as early cancer & heart disease, mother to child human immunodeficiency virus transmission, women's health initiative dietary, and AIDS.
As the newest major resource in the field of medical research, Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs presents a timely and authoritative reviewof the central statistical concepts used to build clinical trials that obtain the best results. The referenceunveils modern approaches vital to understanding, creating, and evaluating data obtained throughoutthe various stages of clinical trial design and analysis.
Accessible and comprehensive, the first volume in a two-part set includes newly-written articles as well as established literature from the Wiley Encyclopedia of Clinical Trials. Illustrating a variety of statistical concepts and principles such as longitudinal data, missing data, covariates, biased-coin randomization, repeated measurements, and simple randomization, the book also provides in-depth coverage of the various trial designs found within phase I-IV trials. Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs also features:
- Detailed chapters on the type of trial designs, such as adaptive, crossover, group-randomized, multicenter, non-inferiority, non-randomized, open-labeled, preference, prevention, and superiority trials
- Over 100 contributions from leading academics, researchers, and practitioners
- An exploration of ongoing, cutting-edge clinical trials on early cancer and heart disease, mother-to-child human immunodeficiency virus transmission trials, and the AIDS Clinical Trials Group
Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs is an excellent reference for researchers, practitioners, and students in the fields of clinicaltrials, pharmaceutics, biostatistics, medical research design, biology, biomedicine, epidemiology,and public health.
N. BALAKRISHNAN, PhD, is Professor in the Department of Mathematics and Statistics at McMaster University, Canada. He is the author of over twenty books and is the coeditor of Encyclopedia of Statistical Sciences, Second Edition, also published by Wiley.
Contributors xxiii
Preface xxix
1 Absolute Risk Reduction 1
1.1 Introduction 1
1.2 Preliminary Issues 1
1.3 Point and Interval Estimates for a Single Proportion 2
1.4 An Unpaired Difference of Proportions 5
1.5 Number Needed to Treat 8
1.6 A Paired Difference of Proportions 10
References 11
Further Reading 12
2 Accelerated Approval 14
2.1 Introduction 14
2.2 Accelerated Development Versus Expanded Access in the U.S.A 14
2.3 Sorting the Terminology—Which FDA Initiatives Do What? 15
2.4 Accelerated Approval Regulations: 21 C.F.R. 314.500, 314.520, 601.40 16
2.5 Stages of Drug Development and FDA Initiatives 16
2.6 Accelerated Approval Regulations: 21 CFR 314.500, 314.520, 601.40 17
2.7 Accelerated Approval with Surrogate Endpoints 18
2.8 Accelerated Approval with Restricted Distribution 20
2.9 Phase IV Studies/Post Marketing Surveillance 20
2.10 Benefit Analysis for Accelerated Approvals Versus Other Illnesses 21
2.11 Problems, Solutions, and Economic Incentives 22
2.12 Future Directions 24
References 25
Further Reading 26
3 AIDS Clinical Trials Group (ACTG) 27
3.1 Introduction 27
3.2 A Brief Primer on HIV/AIDS 27
3.3 ACTG Overview 28
3.4 ACTG Scientific Activities 29
3.5 Development of Potent Antiretroviral Therapy (ART) 29
3.6 Expert Systems and Infrastructure 36
References 37
4 Algorithm-Based Designs 40
4.1 Phase I Dose-Finding Studies 40
4.2 Accelerated Designs 43
4.3 Model-Based Approach in the Estimation of MTD 46
4.4 Exploring Algorithm-Based Designs With Prespecified Targeted Toxicity Levels 48
References 51
5 Alpha-Spending Function 53
5.1 Introduction 53
5.2 Alpha Spending Function Motivation 54
5.3 The Alpha Spending Function 56
5.4 Application of the Alpha Spending Function 57
5.5 Confidence Intervals and Estimation 59
5.6 Trial Design 59
5.7 Conclusions 61
References 61
Further Reading 63
6 Application of New Designs in Phase I Trials 65
6.1 Introduction 65
6.2 Objectives of a Phase I Trial 65
6.3 Standard Designs and Their Shortcomings 66
6.4 Some Novel Designs 67
6.5 Discussion 72
References 72
Further Reading 73
7 ASCOT Trial 74
7.1 Introduction 74
7.2 Objectives 74
7.3 Study Design 74
7.4 Results 75
7.5 Discussion and Conclusions 77
References 78
8 Benefit/Risk Assessment in Prevention Trials 80
8.1 Introduction 80
8.2 Types of B/RAs Performed in Prevention Trials 81
8.3 Alternative Structures of the Benefit/Risk Algorithm used in Prevention Trials 82
8.4 Methodological and Practical Issues with B/RA in Prevention Trials 84
References 87
9 Biased Coin Randomization 90
9.1 Randomization Strategies for Overall Treatment Balance 90
9.2 The Biased Coin Randomization Procedure 91
9.3 Properties 92
9.4 Extensions to the Biased Coin Randomization 92
9.5 Adaptive Biased Coin Randomization 94
9.6 Urn Models 99
9.7 Treatment Balance for Covariates 102
9.8 Application of Biased Coin Designs to Response-Adaptive Randomization 103
References 104
10 Biological Assay, Overview 106
10.1 Introduction 106
10.2 Direct Dilution Assays 108
10.3 Indirect Dilution Assays 109
10.4 Indirect Quantal Assays 113
10.5 Stochastic Approximation in Bioassay 116
10.6 Radioimmunoassay 117
10.7 Dosimetry and Bioassay 118
10.8 Semiparametrics in Bioassays 119
10.9 Nonparametrics in Bioassays 119
10.10 Bioavailability and Bioequivalence Models 120
10.11 Pharmacogenomics in Modern Bioassays 121
10.12 Complexities in Bioassay Modeling and Analysis 122
References 122
Further Reading 124
11 Block Randomization 125
11.1 Introduction 125
11.2 Simple Randomization 125
11.3 Restricted Randomization Through the Use of Blocks 126
11.4 Schemes Using a Single Block for the Whole Trial 130
11.5 Use of Unequal and Variable Block Sizes 131
11.6 Inference and Analysis Following Blocked Randomization 134
11.7 Miscellaneous Topics Related to Blocked Randomization 135
References 136
Further Reading 138
12 Censored Data 139
12.1 Introduction 139
12.2 Independent Censoring 140
12.3 Likelihoods: Noninformative Censoring 143
12.4 Other Kinds of Incomplete Observation 143
References 141
13 Clinical Data Coordination 146
13.1 Introduction 146
13.2 Study Initiation 147
13.3 Study Conduct 151
13.4 Study Closure 158
13.5 Summary 161
References 162
14 Clinical Data Management 164
14.1 Introduction 164
14.2 How Has Clinical Data Management Evolved? 165
14.3 Electronic Data Capture 166
14.4 Regulatory Involvement with Clinical Data Management 167
14.5 Professional Societies 167
14.6 Look to the Future 168
14.7 Conclusion 169
References 169
15 Clinical Significance 170
15.1 Introduction 170
15.2 Historical Background 170
15.3 Article Outline 171
15.4 Design and Methodology 171
15.5 Examples 181
15.6 Recent Developments 181
15.7 Concluding Remarks 185
References 185
16 Clinical Trial Misconduct 191
16.1 The Scope of this Article 191
16.2 Why Does Research Misconduct Matter? 191
16.3 Early Cases 192
16.4 Definition 193
16.5 Intent 194
16.6 What Scientific Misconduct was Not 194
16.7 The Process 194
16.8 The Past Decade 195
16.9 Lessons from the U.S. Experience 196
16.10 Outside the United States 197
16.11 Scientific Misconduct During Clinical Trials 198
16.12 Audit 198
16.13 Causes 199
16.14 Prevalence 200
16.15 Peer Review and Misconduct 200
16.16 Retractions 201
16.17 Prevention 201
References 202
17 Clinical Trials, Early Cancer and Heart Disease 205
17.1 Introduction 205
17.2 Developments in Clinical Trials at the National Cancer Institute (NCI) 205
17.3 Developments in Clinical Trials at the National Heart, Lung, and Blood Institute (NHLBI) 209
References 213
18 Cluster Randomization 216
18.1 Introduction 216
18.2 Examples of Cluster Randomization Trials 217
18.3 Principles of Experimental Design 218
18.4 Experimental and Quasi-Experimental Designs 219
18.5 The Effect of Failing to Replicate 220
18.6 Sample Size Estimation 221
18.7 Cluster Level Analyses 222
18.8 Individual Level Analyses 223
18.9 Incorporating Repeated Assessments 225
18.10 Study Reporting 226
18.11 Meta-Analysis 227
References 228
19 Coherence in Phase I Clinical Trials 230
19.1 Introduction 230
19.2 Coherence: Definitions and Organization 230
19.3 Coherent Designs 232
19.4 Compatible Initial Design 233
19.5 Group Coherence 234
19.6 Real-Time Coherence 235
19.7 Discussion 238
References 238
20 Compliance and Survival Analysis 240
20.1 Compliance: Cause and Effect 240
20.2 All-or-Nothing Compliance 241
20.3 More General Exposure Patterns 242
20.4 Other Structural Modeling Options 242
References 244
21 Composite Endpoints in Clinical Trials 246
21.1 Introduction 246
21.2 The Rationale for Composite Endpoints 246
21.3 Formulation of Composite Endpoints 247
21.4 Examples 248
21.5 Interpreting Composite Endpoints 250
21.6 Conclusions 251
References 251
22 Confounding 252
22.1 Introduction 252
22.2 Confounding as a Bias in Effect Estimation 252
22.3 Confounding and Noncollapsibility 258
22.4 Confounding in Experimental Design 260
References 261
23 Control Groups 263
23.1 Introduction 263
23.2 History 263
23.3 Ethics 264
23.4 Types of Control Groups: Historical Controls 266
23.5 Types of Control Groups: Randomized Controls 268
23.6 Conclusion 271
References 271
24 Coronary Drug Project 273
24.1 Introduction 273
24.2 Objectives 273
24.3 Study Design and Methods 273
24.4 Results 275
24.5 Conclusions and Lessons Learned 281
References 282
Further Reading 284
25 Covariates 285
25.1 Universal Character of Covariates 285
25.2 Use of Covariates in Clinical Trials 286
25.3 Continuous Covariates: Categorization or Functional Form? 293
25.4 Reporting and Summary Assessment of Prognostic Markers 295
References 296
26 Crossover Design 300
26.1 Introduction 300
26.2 The Two-Period, Two-Treatment Design 301
26.3 Higher Order Designs 304
26.4 Model-Based Analyses 307
References 308
27 Crossover Trials 310
27.1 Introduction 310
27.2 2 x 2 Crossover Trial 312
27.3 Higher-Order Designs for Two Treatments 312
27.4 Designs for Three or More Treatments 312
27.5 Analysis of Continuous Data 314
27.6 Analysis of Discrete Data 315
27.7 Concluding Remarks 317
References 317
28 Diagnostic Studies 320
28.1 Introduction 320
28.2 Diagnostic Studies 320
28.3 Reliability 324
28.4 Validity 331
References 338
Further Reading 339
29 DNA Bank 340
29.1 Definition and Objectives of DNA Biobanks 340
29.2 Types of DNA Biobanks 343
29.3 Types of Samples Stored 344
29.4 Quality Assurance and Quality Control in DNA Biobanks 345
29.5 Ethical Issues 346
29.6 Current Biobank Initiatives 348
29.7 Conclusions 350
References 350
30 Up-and-Down and Escalation Designs 353
30.1 Introduction 353
30.2 Up-and-Down Designs 353
30.3 Escalation Designs 357
30.4 Comparing U&D, Escalation and Model-Based Designs 359
References 359
Further Reading 361
31 Dose Ranging Crossover Designs 362
31.1 Introduction 362
31.2 Titration Designs and Extension Studies 369
31.3 Randomized Designs 373
31.4 Discussion and Conclusion 376
References 379
Further Reading 382
32 Flexible Designs 383
32.1 Introduction 383
32.2 The General Framework 384
32.3 Conditional Power and Sample Size Reassessment 387
32.4 Extending the Flexibility to the Choice of the Number of Stages 392
32.5 Selection of the Test Statistic 393
32.6 More General Adaptations and Multiple Hypotheses Testing 393
32.7 An Example 395
32.8 Conclusion 395
References 396
33 Gene Therapy 399
33.1 Introduction 399
33.2 Requirements for Successful Therapeutic Intervention 399
33.3 Pre-Clinical Research 402
33.4 Translational Challenges of Gene Therapy Trials 404
33.5 Clinical Trials · 407
33.6 Lessons Learned 408
33.7 The Way Forward 411
References 411
Further Reading 422
34 Global Assessment Variables 423
34.1 Introduction 423
34.2 Scientific Questions for Multiple Outcomes 423
34.3 General Comments on the GST 424
34.4 Recoding Outcome Measures 424
34.5 Types of Global Statistical Tests (GSTs) 425
34.6 Other Considerations 428
34.7 Other Methods 430
34.8 Examples of the Application of GST 434
34.9 Conclusions 435
References 435
35 Good Clinical Practice (GCP) 438
35.1 Introduction 438
35.2 Human Rights and Protections 438
35.3 Informed Consent 439
35.4 Investigational Protocol 439
35.5 Investigator's Brochure 440
35.6 Investigational New Drug Application 440
35.7 Production of the Investigational Drug 440
35.8 Clinical Testing 441
35.9 Sponsors 442
35.10 Contract Research Organization 444
35.11 Monitors 444
35.12 Investigators 444
35.13 Documentation 444
35.14 Clinical Holds 445
35.15 Inspections/Audits 446
References 446
Further Reading 446
36 Group-Randomized Trials 448
36.1 Introduction 448
36.2 Group-Randomized Trials in Context 449
36.3 The Development of Group-Randomized Trials in Public Health 450
36.4 The Range of GRTs in Public Health 451
36.5 Current Design and Analytic Practices in GRTs in Public Health 452
36.6 The Future of Group-Randomized Trials 453
36.7 Planning a New Group-Randomized Trial 456
References 462
37 Group Sequential Designs 467
37.1 Introduction 467
37.2 Classical Designs 469
37.3 The á-Spending Function Approach 474
37.4 Point Estimates and Confidence Intervals 477
37.5 Supplements 478
References 479
38 Hazard Ratio 483
38.1 Introduction 483
38.2 Definitions 483
38.3 Illustration of Hazard Rate, Hazard Ratio and Risk Ratio 484
38.4 Example on the Use and Usefulness of Hazard Ratios 486
38.5 Ad-hoc Estimator of the Hazard Ratio 486
38.6 Confidence Interval of the Ad-hoc Estimator 487
38.7 Ad-hoc Estimator Stratified for the Covariate Renal Function 491
38.8 Properties of the Ad-hoc Estimator 493
38.9 Class of Generalized Rank Estimators of the Hazard Ratio 493
38.10 Estimation of the Hazard Ratio with Cox's Proportional Hazards Model 494
38.11 Discussion 497
Further Reading 499
References 499
39 Large Simple Trials 500
39.1 Large, Simple Trials 500
39.2 Small but Clinically Important Objective 500
39.3 Eligibility 502
39.4 Randomized Assignment 502
39.5 Outcome Measures 504
39.6 Conclusions 506
References 506
Further Reading 508
40 Longitudinal Data 510
40.1 Definition 510
40.2 Longitudinal Data from Clinical Trials 510
40.3 Advantages 512
40.4 Challenges 512
40.5 Analysis of Longitudinal Data 513
References 514
Further Reading 514
41 Maximum Duration and Information Trials 515
41.1 Introduction 515
41.2 Two Paradigms: Duration versus Information 516
41.3 Sequential Studies: Maximum Duration versus Information Trials 516
41.4 An Example of a Maximum Information Trial 519
References 521
42 Missing Data 522
42.1 Introduction 522
42.2 Methods in Common Use 524
42.3 An Alternative Approach to Incomplete Data 525
42.4 Illustration: Orthodontic Growth Data 527
42.5 Inverse Probability Weighting 531
42.6 Multiple Imputation 531
42.7 Sensitivity Analysis 532
42.8 Conclusion 533
References 533
43 Mother to Child Human Immunodeficiency Virus Transmission Trials 536
43.1 Introduction 536
43.2 The Pediatric Aids Clinical Trials Group 076 Trial 538
43.3 Results 538
43.4 The European Mode of Delivery Trial 540
43.5 The HIV Network for Prevention Trials 012 Trial 541
43.6 The Mashi Trial 544
References 545
Further Reading 549
44 Multiple Testing in Clinical Trials 550
44.1 Introduction 550
44.2 Concepts of Error Rates 551
44.3 Union-Intersection Testing 552
44.4 Closed Testing 553
44.5 Partition Testing 555
References 556
Further Reading 557
45 Multicenter Trials 558
45.1 Definitions 558
45.2 History 560
45.3 Examples 561
45.4 Organizational and Operational Features 563
45.5 Strengths 564
45.6 Counts 565
Readings 569
References 569
46 Multiple Endpoints 570
46.1 Introduction 570
46.2 Multiple Testing Methods 571
46.3 Multivariate Global Tests 573
46.4 Conclusions 574
References 575
47 Multiple Risk Factor Intervention Trial 577
47.1 Introduction 577
47.2 Trial Design 577
47.3 Trial Screening and Execution 579
47.4 Findings at the End of Intervention 580
47.5 Long-Term Follow-Up 581
47.6 Epidemiologie Findings from Long-Term Follow-up of 361,662 MRFIT Screenees 582
47.7 Conclusions 583
References 583
Further Reading 586
48 N-of-1 Randomized Trials 587
48.1 Introduction 587
48.2 Goal of N-of-1 Studies 587
48.3 Requirements 588
48.4 Design Choices and Details for N-of-1 Studies 589
48.5 Statistical Issues 592
48.6 Other Issues 593
48.7 Conclusions 596
References 596
49 Noninferiority Trial 598
49.1 Introduction 598
49.2 Essential Elements of Noninferiority Trial Design 598
49.3 Objectives of Noninferiority Trials 600
49.4 Measure of Treatment Effect 600
49.5 Noninferiority Margin 601
49.6 Statistical Testing for Noninferiority 603
49.7 Medication Nonadherence and Misclassificat ion/Measurement Error 604
49.8 Testing Superiority and Noninferiority 605
49.9 Conclusion 605
References 605
50 Nonrandomized Trials 609
50.1 Introduction 609
50.2 Randomized vs. Nonrandomized Clinical Trials 609
50.3 Control Groups in Nonrandomized Trials 611
50.4 Statistical Methods in Design and Analyses 613
50.5 Conclusion and Discussion 616
References 617
51 Open-Labeled Trials 619
51.1 Introduction 619
51.2 The Importance of Blinding 619
51.3 Reasons Why Trials Might Have to be Open-Label 622
51.4 When Open-Label Trials Might be Desirable 623
51.5 Concluding Comments 623
References 623
Further Reading 624
52 Optimizing Schedule of Administration in Phase I Clinical Trials 625
52.1 Introduction 625
52.2 Motivating Example 627
52.3 Design Issues 627
52.4 Trial Conduct 631
52.5 Extensions and Related Research 632
References 632
53 Partially Balanced Designs 635
53.1 Introduction 635
53.2 Association Schemes 635
53.3 Partially Balanced Incomplete Block Designs 641
53.4 Generalizations of PBIBDs and Related Ideas 648
References 655
54 Phase I/II Clinical Trials 658
54.1 Introduction 658
54.2 Traditional Approach 659
54.3 Recent Developments 660
54.4 Illustrations 663
References 665
55 Phase II/III Trials 667
55.1 Introduction 667
55.2 Description and Legal Basis 668
55.3 Better Dose-Response Studies with Phase 2/3 Designs 672
55.4 Principles of Phase 2/3 Designs 673
55.5 Inferential Difficulties 676
55.6 Summary 678
References 679
Further Reading 680
56 Phase I Trials 682
56.1 Introduction 682
56.2 Phase I in Healthy Volunteers 683
56.3 Phase I in Cancer Patients 684
56.4 Perspectives in the Future of Cancer Phase I Trials 687
56.5 Discussion 688
References 688
57 Phase II Trials 692
57.1 Introduction 692
57.2 Proof-of-Concept (Phase Ha) Trials 693
57.3 Dose-Ranging (Phase lib) Trials 695
57.4 Efficacy Endpoints 697
57.5 Oncology Phase II Trials 697
References 697
Further Reading 699
58 Phase III Trials 700
58.1 Introduction 700
58.2 Research Methodology in Phase III 700
58.3 Type of Design 706
58.4 Discussion 708
References 709
59 Phase IV Trials 711
59.1 Introduction 711
59.2 Definitions and Context 711
59.3 Different Purposes for Phase IV Trials 712
59.4 Essential and Desirable Features of Phase IV Trials 715
59.5 Examples of Phase IV Studies 715
59.6 Conclusion 717
References 717
Further Reading 718
60 Phase I Trials in Oncology 719
60.1 Introduction 719
60.2 Dose-Limiting Toxicity 719
60.3 Starting Dose 720
60.4 Dose Level Selection 720
60.5 Study Design and General Considerations 720
60.6 Traditional, Standard, or 3 + 3 Design 721
60.7 Continual Reassessment Method and Other Designs that Target the MTD722
60.8 Start-Up Rule 722
60.9 Phase I Trials with Long Follow-Up 722
60.10 Phase I Trials with Multiple Agents 723
60.11 Phase I Trials with the MTD Defined using Toxicity Grades 723
References 723
Further Reading 724
61 Placebos 725
61.1 History of Placebo 725
61.2 Definitions 725
61.3 Magnitude of the Placebo Effect 726
61.4 Influences on the Placebo Effect 727
61.5 Ethics of Employing Placebo in Research 728
61.6 Guidelines for the Use of Placebos in Research 729
61.7 Innovations to Improve Research Involving Placebo 731
61.8 Summary 732
References 732
62 Planning a Group-Randomized Trial 736
62.1 Introduction 736
62.2 The Research Question 736
62.3 The Research Team 737
62.4 The Research Design 737
62.5 Potential Design Problems and Methods to Avoid Them 738
62.6 Potential Analytic Problems and Methods to Avoid Them 739
62.7 Variables of Interest and Their Measures 739
62.8 The Intervention 740
62.9 Power 742
62.10 Summary 742
References 743
63 Postmenopausal Estrogen/Progestin Interventions Trial (PEPI) 744
63.1 Introduction 744
63.2 Design and Objectives 744
63.3 Study Design 746
63.4 Outcomes 747
63.5 Results 749
63.6 Conclusions 753
References 754
Further Reading 756
64 Preference Trials 759
64.1 Introduction 759
64.2 Potential Effects of Preference 759
64.3 The Patient Preference Design 761
64.4 Advantages and Disadvantages of the Patient Preference Design 761
64.5 Alternative Designs 764
64.6 Discussion 767
References 768
Further Reading 769
65 Prevention Trials 770
65.1 Introduction 770
65.2 Role Among Possible Research Strategies 771
65.3 Prevention Trial Planning and Design 773
65.4 Conduct, Monitoring, and Analysis 775
References 776
66 Primary Efficacy Endpoint 779
66.1 Defining the Primary Endpoint 779
66.2 Fairness of Endpoints 780
66.3 Specificity of the Primary Endpoint 782
66.4 Composite Primary Endpoints 782
66.5 Missing Primary Endpoint Data 784
66.6 Censored Primary Endpoints 784
66.7 Surrogate Primary Endpoints 785
66.8 Multiple Primary Endpoints 786
66.9 Secondary Endpoints 786
References 786
Further Reading 788
67 Prognostic Variables in Clinical Trials 789
67.1 Introduction 789
67.2 A General Theory of Prognostic Variables 791
67.3 Valid Covariates and Recognizable Subsets 792
67.4 Stratified Randomization and Analysis 793
67.5 Statistical Importance of Prognostic Factors 795
References 797
68 Randomization Procedures 799
68.1 Basics 799
68.2 General Classes of Randomization: Complete Versus Imbalance-Restricted Procedures 800
68.3 Procedures for Imbalance-Restricted Randomization 801
68.4 Randomization-Based Analysis and the Validation Transformation 809
68.5 Conclusions 810
References 810
69 Randomization Schedule 813
69.1 Introduction 813
69.2 Preparing the Schedule 814
69.3 Schedules for Open-Label Trials 817
69.4 Schedules to Mitigate Loss of Balance in Treatment Assignments Because of Incomplete Blocks 818
69.5 Issues Related to the use of Randomization Schedule 822
69.6 Summary 824
References 825
Further Reading 826
70 Repeated Measurements 827
70.1 Introduction and Case Study 827
70.2 Linear Models for Gaussian Data 828
70.3 Models for Discrete Outcomes 831
70.4 Design Considerations 836
70.5 Concluding Remarks 837
References 838
71 Simple Randomization 841
71.1 Introduction 841
71.2 Concept of Randomization 841
71.3 Why is Randomization Needed? 842
71.4 Methods: Simple Randomization 842
71.5 Advantages and Disadvantages of Randomization 845
71.6 Other Randomization Methods 846
71.7 Stratified Randomization 846
References 849
Further Reading 849
72 Subgroups 850
72.1 Introduction 850
72.2 The General Problem 851
72.3 Definitions 851
72.4 Subgroup Effects and Interactions 852
72.5 Tests of Interactions and the Problem of Power 853
72.6 Subgroups and the Problem of Multiple Comparisons 856
72.7 Demographic Subgroups 858
72.8 Physiological Subgroups 861
72.9 Target Subgroups 861
72.10 Improper Subgroups 863
72.11 Summary 865
References 865
73 Superiority Trials 867
73.1 Introduction 867
73.2 Clinicians Ask One-Sided Questions, and Want Immediate Answers 867
73.3 But Traditional Statistics Is Two-Sided 867
73.4 The Consequences of Two-Sided Answers to One-Sided Questions 868
73.5 The Fallacy of the "Negative" Trial 868
73.6 The Solution Lies in Employing One-Sided Statistics 868
73.7 Examples of Employing One-Sided Statistics 868
73.8 One-Sided Statistical Analyses Need to be Specified Ahead of Time 869
73.9 A Graphic Demonstration of Superiority and Noninferiority 869
73.10 How to Think about and Incorporate Minimally Important Differences 870
73.11 Incorporating Confidence Intervals for Treatment Effects 871
73.12 Why We Should Never Label an "Indeterminate" Trial Result as "Negative" or as Showing "No Effect" 871
73.13 How Does a Treatment Become "Established Effective Therapy"? 872
73.14 Most Trials are Too Small to Declare a Treatment "Established Effective Therapy" 872
73.15 How Do We Achieve a Superiority Result? 872
73.16 Superiority and Noninferiority Trials when Established Effective Therapy Already Exists 872
73.17 Exceptions to the Rule that It Is Always Unethical to Substitute Placebos for Established Effective Therapy 873
73.18 When a Promising New Treatment Might be Added to Established Effective Therapy 873
73.19 Using Placebos in a Trial Should Not Mean the Absence of Treatment 874
73.20 Demonstrating Trials of Promising New Treatments Against (or in Addition to) Established Effective Therapy 874
73.21 Why We Almost Never Find, and Rarely Seek, True "Equivalence" 874
73.22 The Graphical Demonstration of "Superiority" and "Noninferiority" 876
73.23 Completing the Circle: Converting One-Sided Clinical Thinking into One-Sided Statistical Analysis 876
73.24 A Final Note on Superiority and Noninferiority Trials of "Me-Too" Drugs 877
References 877
Further Reading 877
74 Surrogate Endpoints 878
74.1 Introduction 878
74.2 Illustrations 879
74.3 Validation of Surrogates 880
74.4 Auxiliary Variables 883
74.5 Conclusions 884
References 885
75 TNT Trial 887
75.1 Introduction 887
75.2 Objectives 887
75.3 Study Design 887
75.4 Results 888
75.5 Conclusions 892
References 892
Further Reading 893
76 UGDP Trial 894
76.1 Introduction 894
76.2 Design and Chronology 895
76.3 Results 906
76.4 Conclusion and Discussion 909
References 914
77 Women's Health Initiative Hormone Therapy Trials 918
77.1 Introduction 918
77.2 Objectives 918
77.3 Study Design 918
77.4 Results 919
77.5 Conclusions 927
References 928
78 Women's Health Initiative Dietary Modification Trial 931
78.1 Rationale for Biomarker Calibration of Self-Report Measures of Diet 931
78.2 Nutrient Biomarker Study Energy and Protein Calibration 932
78.3 Measurement Error Properties of 4DFR, 24HR, and FFQ 933
78.4 Calibration of Self-Report Measures of Physical Activity 933
78.5 Psychosocial Measures and Biomarker-Calibrated Intake 936
78.6 Calibrated Energy, Protein, Protein Density, and Cardiovascular Disease Incidence 937
78.7 Diabetes and Calibrated Consumption 938
78.8 Cancer and Calibrated Intake 940
78.9 Associations Between Protein Intake, Frailty, and Renal Function 940
78.10 Summary and Future Directions 941
References 943
Index 945
| Erscheint lt. Verlag | 25.3.2014 |
|---|---|
| Reihe/Serie | Methods and Applications of Statistics |
| Zusatzinfo | illustrations |
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 186 x 252 mm |
| Gewicht | 1820 g |
| Einbandart | gebunden |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| Medizin / Pharmazie ► Medizinische Fachgebiete | |
| Studium ► Querschnittsbereiche ► Epidemiologie / Med. Biometrie | |
| Schlagworte | Klinische Studien • Medizinische Statistik |
| ISBN-10 | 1-118-30473-X / 111830473X |
| ISBN-13 | 978-1-118-30473-0 / 9781118304730 |
| Zustand | Neuware |
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