How This Book Is Organized

Mark Chang
Millennium Pharmaceuticals
Cambridge, Massachusetts, U.S.A.

Preface vii
1. Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Adaptive Design Methods in Clinical Trials . . . . . . . . . 2
1.2.1 Group Sequential Design . . . . . . . . . . . . . . . . 3
1.2.2 Sample-Size Re-estimation Design . . . . . . . . . . . 4
1.2.3 Drop-Loser Design . . . . . . . . . . . . . . . . . . . 5
1.2.4 Adaptive Randomization Design . . . . . . . . . . . . 6
1.2.5 Adaptive Dose-Finding Design . . . . . . . . . . . . . 6
1.2.6 Biomarker-Adaptive Design . . . . . . . . . . . . . . 7
1.2.7 Adaptive Treatment-Switching Design . . . . . . . . 8
1.2.8 Clinical Trial Simulation . . . . . . . . . . . . . . . . 9
1.2.9 Regulatory Aspects . . . . . . . . . . . . . . . . . . . 11
1.2.10 Characteristics of Adaptive Designs . . . . . . . . . . 12
1.3 FAQs about Adaptive Designs . . . . . . . . . . . . . . . . . 13
1.4 Roadmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2. Classic Design 19
2.1 Overview of Drug Development . . . . . . . . . . . . . . . . 19
2.2 Two-Group Superiority and Noninferiority Designs . . . . . 21
2.2.1 General Approach to Power Calculation . . . . . . . 21
2.2.2 Powering Trials Appropriately . . . . . . . . . . . . . 26
2.3 Two-Group Equivalence Trial . . . . . . . . . . . . . . . . . 28
2.3.1 Equivalence Test . . . . . . . . . . . . . . . . . . . . 28
2.3.2 Average Bioequivalence . . . . . . . . . . . . . . . . 32
2.3.3 Population and Individual Bioequivalence . . . . . . 34
2.4 Dose-Response Trials . . . . . . . . . . . . . . . . . . . . . . 35
xiii
xiv Adaptive Design Theory and Implementation
2.4.1 Uni…ed Formulation for Sample-Size . . . . . . . . . 36
2.4.2 Application Examples . . . . . . . . . . . . . . . . . 38
2.4.3 Determination of Contrast Coe¢ cients . . . . . . . . 41
2.4.4 SAS Macro for Power and Sample-Size . . . . . . . . 43
2.5 Maximum Information Design . . . . . . . . . . . . . . . . . 45
2.6 Summary and Discussion . . . . . . . . . . . . . . . . . . . 45
3. Theory of Adaptive Design 51
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2 General Theory . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2.1 Stopping Boundary . . . . . . . . . . . . . . . . . . . 54
3.2.2 Formula for Power and Adjusted P-value . . . . . . . 55
3.2.3 Selection of Test Statistics . . . . . . . . . . . . . . . 57
3.2.4 Polymorphism . . . . . . . . . . . . . . . . . . . . . . 57
3.2.5 Adjusted Point Estimates . . . . . . . . . . . . . . . 59
3.2.6 Derivation of Con…dence Intervals . . . . . . . . . . . 62
3.3 Design Evaluation - Operating Characteristics . . . . . . . . 64
3.3.1 Stopping Probabilities . . . . . . . . . . . . . . . . . 64
3.3.2 Expected Duration of an Adaptive Trial . . . . . . . 64
3.3.3 Expected Sample Sizes . . . . . . . . . . . . . . . . . 65
3.3.4 Conditional Power and Futility Index . . . . . . . . . 65
3.3.5 Utility and Decision Theory . . . . . . . . . . . . . . 66
3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4. Method with Direct Combination of P-values 71
4.1 Method Based on Individual P-values . . . . . . . . . . . . 71
4.2 Method Based on the Sum of P-values . . . . . . . . . . . . 76
4.3 Method with Linear Combination of P-values . . . . . . . . 81
4.4 Method with Product of P-values . . . . . . . . . . . . . . . 81
4.5 Event-Based Adaptive Design . . . . . . . . . . . . . . . . . 93
4.6 Adaptive Design for Equivalence Trial . . . . . . . . . . . . 95
4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5. Method with Inverse-Normal P-values 101
5.1 Method with Linear Combination of Z-Scores . . . . . . . . 101
5.2 Lehmacher and Wassmer Method . . . . . . . . . . . . . . . 104
5.3 Classic Group Sequential Method . . . . . . . . . . . . . . . 109
5.4 Cui-Hung-Wang Method . . . . . . . . . . . . . . . . . . . . 112
5.5 Lan-DeMets Method . . . . . . . . . . . . . . . . . . . . . . 113
5.5.1 Brownian Motion . . . . . . . . . . . . . . . . . . . . 113
Contents xv
5.5.2 Lan-DeMets Error-Spending Method . . . . . . . . . 115
5.6 Fisher-Shen Method . . . . . . . . . . . . . . . . . . . . . . 118
5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
6. Implementation of K-Stage Adaptive Designs 121
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6.2 Nonparametric Approach . . . . . . . . . . . . . . . . . . . 121
6.2.1 Normal Endpoint . . . . . . . . . . . . . . . . . . . . 121
6.2.2 Binary Endpoint . . . . . . . . . . . . . . . . . . . . 127
6.2.3 Survival Endpoint . . . . . . . . . . . . . . . . . . . . 131
6.3 Error-Spending Approach . . . . . . . . . . . . . . . . . . . 137
6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
7. Conditional Error Function Method 139
7.1 Proschan-Hunsberger Method . . . . . . . . . . . . . . . . . 139
7.2 Denne Method . . . . . . . . . . . . . . . . . . . . . . . . . 142
7.3 Müller-Schäfer Method . . . . . . . . . . . . . . . . . . . . . 143
7.4 Comparison of Conditional Power . . . . . . . . . . . . . . . 143
7.5 Adaptive Futility Design . . . . . . . . . . . . . . . . . . . . 149
7.5.1 Utilization of an Early Futility Boundary . . . . . . . 149
7.5.2 Design with a Futility Index . . . . . . . . . . . . . . 150
7.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
8. Recursive Adaptive Design 153
8.1 P-clud Distribution . . . . . . . . . . . . . . . . . . . . . . . 153
8.2 Two-Stage Design . . . . . . . . . . . . . . . . . . . . . . . 155
8.2.1 Method Based on Product of P-values . . . . . . . . 156
8.2.2 Method Based on Sum of P-values . . . . . . . . . . 157
8.2.3 Method Based on Inverse-Normal P-values . . . . . . 158
8.2.4 Con…dence Interval and Unbiased Median . . . . . . 159
8.3 Error-Spending and Conditional Error Principles . . . . . . 163
8.4 Recursive Two-Stage Design . . . . . . . . . . . . . . . . . . 165
8.4.1 Sum of Stagewise P-values . . . . . . . . . . . . . . . 166
8.4.2 Product of Stagewise P-values . . . . . . . . . . . . . 168
8.4.3 Inverse-Normal Stagewise P-values . . . . . . . . . . 168
8.4.4 Con…dence Interval and Unbiased Median . . . . . . 169
8.4.5 Application Example . . . . . . . . . . . . . . . . . . 170
8.5 Recursive Combination Tests . . . . . . . . . . . . . . . . . 174
8.6 Decision Function Method . . . . . . . . . . . . . . . . . . . 177
8.7 Summary and Discussion . . . . . . . . . . . . . . . . . . . 178
xvi Adaptive Design Theory and Implementation
9. Sample-Size Re-Estimation Design 181
9.1 Opportunity . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
9.2 Adaptation Rules . . . . . . . . . . . . . . . . . . . . . . . . 182
9.2.1 Adjustment Based on E¤ect Size Ratio . . . . . . . . 182
9.2.2 Adjustment Based on Conditional Power . . . . . . . 183
9.3 SAS Macros for Sample-Size Re-estimation . . . . . . . . . 184
9.4 Comparison of Sample-Size Re-estimation Methods . . . . . 187
9.5 Analysis of Design with Sample-Size Adjustment . . . . . . 192
9.5.1 Adjusted P-value . . . . . . . . . . . . . . . . . . . . 192
9.5.2 Con…dence Interval . . . . . . . . . . . . . . . . . . 193
9.5.3 Adjusted Point Estimates . . . . . . . . . . . . . . . 194
9.6 Trial Example: Prevention of Myocardial Infarction . . . . 195
9.7 Summary and Discussion . . . . . . . . . . . . . . . . . . . 199
10. Multiple-Endpoint Adaptive Design 203
10.1Multiplicity Issues . . . . . . . . . . . . . . . . . . . . . . . 203
10.1.1 Statistical Approaches to the Multiplicity . . . . . . 204
10.1.2 Single Step Procedures . . . . . . . . . . . . . . . . . 207
10.1.3 Stepwise Procedures . . . . . . . . . . . . . . . . . . 209
10.1.4 Gatekeeper Approach . . . . . . . . . . . . . . . . . . 211
10.2Multiple-Endpoint Adaptive Design . . . . . . . . . . . . . 213
10.2.1 Fractals of Gatekeepers . . . . . . . . . . . . . . . . . 213
10.2.2 Single Primary with Secondary Endpoints . . . . . . 215
10.2.3 Coprimary with Secondary Endpoints . . . . . . . . 219
10.2.4 Tang-Geller Method . . . . . . . . . . . . . . . . . . 220
10.2.5 Summary and Discussion . . . . . . . . . . . . . . . . 222
11. Drop-Loser and Add-Arm Design 225
11.1 Opportunity . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
11.1.1 Impact Overall Alpha Level and Power . . . . . . . . 225
11.1.2 Reduction In Expected Trial Duration . . . . . . . . 226
11.2Method with Weak Alpha-Control . . . . . . . . . . . . . . 227
11.2.1 Contract Test Based Method . . . . . . . . . . . . . 227
11.2.2 Sampson-Sill’s Method . . . . . . . . . . . . . . . . . 228
11.2.3 Normal Approximation Method . . . . . . . . . . . . 229
11.3Method with Strong Alpha-Control . . . . . . . . . . . . . . 230
11.3.1 Bauer-Kieser Method . . . . . . . . . . . . . . . . . . 230
11.3.2 MSP with Single-Step Multiplicity Adjustment . . . 230
11.3.3 A More Powerful Method . . . . . . . . . . . . . . . 231
11.4 Application of SAS Macro for Drop-Loser Design . . . . . . 232
Contents xvii
11.5 Summary and Discussion . . . . . . . . . . . . . . . . . . . 236
12. Biomarker-Adaptive Design 239
12.1 Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . 239
12.2 Design with Classi…er Biomarker . . . . . . . . . . . . . . . 241
12.2.1 Setting the Scene . . . . . . . . . . . . . . . . . . . . 241
12.2.2 Classic Design with Classi…er Biomarker . . . . . . . 243
12.2.3 Adaptive Design with Classi…er Biomarker . . . . . . 246
12.3 Challenges in Biomarker Validation . . . . . . . . . . . . . . 251
12.3.1 Classic Design with Biomarker Primary-Endpoint . . 251
12.3.2 Treatment-Biomarker-Endpoint Relationship . . . . . 251
12.3.3 Multiplicity and False Positive Rate . . . . . . . . . 253
12.3.4 Validation of Biomarkers . . . . . . . . . . . . . . . . 253
12.3.5 Biomarkers in Reality . . . . . . . . . . . . . . . . . 254
12.4 Adaptive Design with Prognostic Biomarker . . . . . . . . . 255
12.4.1 Optimal Design . . . . . . . . . . . . . . . . . . . . . 255
12.4.2 Prognostic Biomarker in Designing Survival Trial . . 256
12.5 Adaptive Design with Predictive Marker . . . . . . . . . . . 257
12.6 Summary and Discussion . . . . . . . . . . . . . . . . . . . 257
13. Adaptive Treatment Switching and Crossover 259
13.1 Treatment Switching and Crossover . . . . . . . . . . . . . . 259
13.2Mixed Exponential Survival Model . . . . . . . . . . . . . . 260
13.2.1 Mixed Exponential Model . . . . . . . . . . . . . . . 260
13.2.2 E¤ect of Patient Enrollment Rate . . . . . . . . . . . 263
13.2.3 Hypothesis Test and Power Analysis . . . . . . . . . 265
13.3 Threshold Regression . . . . . . . . . . . . . . . . . . . . . . 267
13.3.1 First Hitting Time Model . . . . . . . . . . . . . . . 267
13.3.2 Mixture of Wiener Processes . . . . . . . . . . . . . . 268
13.4 Latent Event Time Model for Treatment Crossover . . . . . 271
13.5 Summary and discussions . . . . . . . . . . . . . . . . . . . 273
14. Response-Adaptive Allocation Design 275
14.1 Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . 275
14.1.1 Play-the-Winner Model . . . . . . . . . . . . . . . . 275
14.1.2 Randomized Play-the-Winner Model . . . . . . . . . 276
14.1.3 Optimal RPW Model . . . . . . . . . . . . . . . . . . 277
14.2 Adaptive Design with RPW . . . . . . . . . . . . . . . . . . 278
14.3 General Response-Adaptive Randomization (RAR) . . . . . 282
14.3.1 SAS Macro for M-Arm RAR with Binary Endpoint . 282
xviii Adaptive Design Theory and Implementation
14.3.2 SAS Macro for M-Arm RAR with Normal Endpoint 285
14.3.3 RAR for General Adaptive Designs . . . . . . . . . . 287
14.4 Summary and Discussion . . . . . . . . . . . . . . . . . . . 288
15. Adaptive Dose Finding Design 291
15.1 Oncology Dose-Escalation Trial . . . . . . . . . . . . . . . . 291
15.1.1 Dose Level Selection . . . . . . . . . . . . . . . . . . 291
15.1.2 Traditional Escalation Rules . . . . . . . . . . . . . . 292
15.1.3 Simulations Using SAS Macro . . . . . . . . . . . . . 295
15.2 Continual Reassessment Method (CRM) . . . . . . . . . . . 297
15.2.1 Probability Model for Dose-Response . . . . . . . . . 298
15.2.2 Prior Distribution of Parameter . . . . . . . . . . . . 298
15.2.3 Reassessment of Parameter . . . . . . . . . . . . . . 299
15.2.4 Assignment of Next Patient . . . . . . . . . . . . . . 300
15.2.5 Simulations of CRM . . . . . . . . . . . . . . . . . . 300
15.2.6 Evaluation of Dose-Escalation Design . . . . . . . . . 302
15.3 Summary and Discussion . . . . . . . . . . . . . . . . . . . 304
16. Bayesian Adaptive Design 307
16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
16.2 Bayesian Learning Mechanism . . . . . . . . . . . . . . . . . 308
16.3 Bayesian Basics . . . . . . . . . . . . . . . . . . . . . . . . . 309
16.3.1 Bayes’Rule . . . . . . . . . . . . . . . . . . . . . . . 309
16.3.2 Conjugate Family of Distributions . . . . . . . . . . 311
16.4 Trial Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 312
16.4.1 Bayesian for Classic Design . . . . . . . . . . . . . . 312
16.4.2 Bayesian Power . . . . . . . . . . . . . . . . . . . . . 315
16.4.3 Frequentist Optimization . . . . . . . . . . . . . . . . 316
16.4.4 Bayesian Optimal Adaptive Designs . . . . . . . . . 318
16.5 Trial Monitoring . . . . . . . . . . . . . . . . . . . . . . . . 322
16.6 Analysis of Data . . . . . . . . . . . . . . . . . . . . . . . . 323
16.7 Interpretation of Outcomes . . . . . . . . . . . . . . . . . . 325
16.8 Regulatory Perspective . . . . . . . . . . . . . . . . . . . . . 327
16.9 Summary and Discussions . . . . . . . . . . . . . . . . . . . 328
17. Planning, Execution, Analysis, and Reporting 331
17.1 Validity and Integrity . . . . . . . . . . . . . . . . . . . . . 331
17.2 Study Planning . . . . . . . . . . . . . . . . . . . . . . . . . 332
17.3Working with Regulatory Agency . . . . . . . . . . . . . . . 332
17.4 Trial Monitoring . . . . . . . . . . . . . . . . . . . . . . . . 333
xix
17.5 Analysis and Reporting . . . . . . . . . . . . . . . . . . . . 334
17.6 Bayesian Approach . . . . . . . . . . . . . . . . . . . . . . . 335
17.7 Clinical Trial Simulation . . . . . . . . . . . . . . . . . . . . 335
17.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
18. Paradox - Debates in Adaptive Designs 339
18.1My Standing Point . . . . . . . . . . . . . . . . . . . . . . . 339
18.2 Decision Theory Basics . . . . . . . . . . . . . . . . . . . . . 340
18.3 Evidence Measure . . . . . . . . . . . . . . . . . . . . . . . 342
18.3.1 Frequentist P-Value . . . . . . . . . . . . . . . . . . . 342
18.3.2 Maximum Likelihood Estimate . . . . . . . . . . . . 342
18.3.3 Bayes Factor . . . . . . . . . . . . . . . . . . . . . . 343
18.3.4 Bayesian P-Value . . . . . . . . . . . . . . . . . . . . 344
18.3.5 Repeated Looks . . . . . . . . . . . . . . . . . . . . . 345
18.3.6 Role of Alpha in Drug Development . . . . . . . . . 345
18.4 Statistical Principles . . . . . . . . . . . . . . . . . . . . . . 346
18.5 Behaviors of Statistical Principles in Adaptive Designs . . . 352
18.5.1 Su¢ ciency Principle . . . . . . . . . . . . . . . . . . 352
18.5.2 Minimum Su¢ ciency Principle and E¢ ciency . . . . 353
18.5.3 Conditionality and Exchangeability Principles . . . . 354
18.5.4 Equal Weight Principle . . . . . . . . . . . . . . . . . 355
18.5.5 Consistency of Trial Results . . . . . . . . . . . . . . 356
18.5.6 Bayesian Aspects . . . . . . . . . . . . . . . . . . . . 357
18.5.7 Type-I Error, P-value, Estimation . . . . . . . . . . . 357
18.5.8 The 0-2-4 Paradox . . . . . . . . . . . . . . . . . . . 358
18.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360
Appendix A Random Number Generation 363
A.1 Random Number . . . . . . . . . . . . . . . . . . . . . . . . 363
A.2 Uniformly Distributed Random Number . . . . . . . . . . . 363
A.3 Inverse CDF Method . . . . . . . . . . . . . . . . . . . . . . 364
A.4 Acceptance-Rejection Methods . . . . . . . . . . . . . . . . 364
A.5 Multi-Variate Distribution . . . . . . . . . . . . . . . . . . . 365
Appendix B Implementing Adaptive Designs in R 369
Bibliography 381
Index 403
xx
List of Figures
Figure 1.1: Trends in NDAs Submitted to FDA
Figure 1.2: Sample-Size Re-Estimation Design
Figure 1.3: Drop-Loser Design
Figure 1.4: Response Adaptive Randomization
Figure 1.5: Dose-Escalation for Maximum Tolerated Dose
Figure 1.6: Biomarker-Adaptive Design
Figure 1.7: Adaptive Treatment Switching
Figure 1.8: Clinical Trial Simulation Model
Figure 1.9: Characteristics of Adaptive Designs
Figure 2.1: A Simpli…ed View of the NDA
Figure 2.2: Power as a Function of a and n
Figure 2.3: Sample-Size Calculation Based on 
Figure 2.4: Power and Probability of E¢ cacy
Figure 2.5: P-value Versus Observed E¤ect Size
Figure 3.1: Various Adaptations
Figure 3.2: Selected Adaptive Design Methods from This Book
Figure 3.3: Bayesian Decision Approach
Figure 5.1: Examples of Brownian Motion
Figure 7.1: Conditional Error Functions
Figure 8.1: Various Stopping Boundaries at Stage 2
Figure 8.2: Recursive Two-stage Adaptive Design
Figure 9.1: Conditional Power Versus P-value from Stage 1
Figure 10.1: Multiple-Endpoint Adaptive Design
Figure 11.1: Seamless Design
Figure 11.2: Decision Theory for Competing Constraints
Figure 12.1: E¤ect of Biomarker Misclassi…cation
Figure 12.2: Treatment-Biomarker-Endpoint Three-Way Relationship
Figure 12.3: Correlation Versus Prediction
Figure 13.1: Di¤erent Paths of Mixed Wiener Process
Figure 14.1: Randomized-Play-the-Winner
Figure 15.1: Logistic Toxicity Model
Figure 16.1: Bayesian Learning Process
Figure 16.2: ExpDesign Studio
Figure 16.3: Interpretation of Con…dence Interval
Figure 17.1: Simpli…ed CTS Model: Gray-Box
Figure 18.1: Illustration of Likelihood Function
List of Examples
Example 2.1 Arteriosclerotic Vascular Disease Trial
Example 2.2 Equivalence LDL Trial
xxi
Example 2.3 Average Bioequivalence Trial
Example 2.4 Dose-Response Trial with Continuous Endpoint
Example 2.5 Dose-Response Trial with Binary Endpoint
Example 2.6 Dose-Response Trial with Survival Endpoint
Example 3.1 Adjusted Con…dence Interval and Point Estimate
Example 4.1 Adaptive Design for Acute Ischemic Stroke Trial
Example 4.2 Adaptive Design for Asthma Study
Example 4.3 Adaptive Design for Oncology Trial
Example 4.4: Early Futility Stopping Design with Binary Endpoint
Example 4.5: Noninferiority Design with Binary Endpoint
Example 4.6: Sample-Size Re-estimation with Normal Endpoint
Example 4.7: Sample-Size Re-estimation with Survival Endpoint
Example 4.8 Adaptive Equivalence LDL Trial
Example 5.1 Inverse-Normal Method with Normal Endpoint
Example 5.2 Inverse-Normal Method with SSR
Example 5.3 Group Sequential Design
Example 5.4 Changes in Number and Timing of Interim Analyses
Example 6.1 Three-Stage Adaptive Design
Example 6.2 Four-Stage Adaptive Design
Example 6.3 Adaptive Design with Survival Endpoint
Example 7.1 Adaptive Design for Coronary Heart Disease Trial
Example 8.1 Recursive Two-Stage Adaptive Design
Example 8.2 Recursive Combination Method
Example 9.1 Myocardial Infarction Prevention Trial
Example 9.2: Adaptive Design with Farrington-Manning NI Margin
Example 10.1 Acute Respiratory Disease Syndrome Trial
Example 10.2 Three-Stage Adaptive Design for NHL Trial
Example 10.3 Design with Multiple Primary-Secondary Endpoints
Example 11.1 Seamless Design of Asthma Trial
Example 12.1 Biomarker-Adaptive Design
Example 13.1 Adaptive Treatment Switching Trial
Example 13.2 Treatment Switching with Uniform Accrual Rate
Example 14.1 Randomized Played-the-Winner Design
Example 14.2 Adaptive Randomization with Normal Endpoint
Example 15.1 Adaptive Dose-Finding for Prostate Cancer Trial
Example 16.1 Beta Posterior Distribution
Example 16.2 Normal Posterior Distribution
Example 16.3 Prior E¤ect on Power
Example 16.4 Power with Normal Prior
Example 16.5 Bayesian Power
Example 16.6 Trial Design Using Bayesian Power
xxii
Example 16.7 Simon Two-Stage Optimal Design
Example 16.8 Bayesian Optimal Design
Example 18.1 Paradox: Binomial and Negative Binomial?
List SAS Macros
SAS Macro 2.1: Equivalence Trial with Normal Endpoint
SAS Macro 2.2: Equivalence Trial with Binary Endpoint
SAS Macro 2.3: Crossover Bioequivalence Trial
SAS Macro 2.4: Sample-Size for Dose-Response Trial
SAS Macro 4.1: Two-Stage Adaptive Design with Binary Endpoint
SAS Macro 4.2: Two-Stage Adaptive Design with Normal Endpoint
SAS Macro 4.3: Two-Stage Adaptive Design with Survival Endpoint
SAS Macro 4.4: Event-Based Adaptive Design
SAS Macro 4.5: Adaptive Equivalence Trial Design
SAS Macro 5.1: Stopping Boundaries with Adaptive Designs
SAS Macro 5.2: Two-Stage Design with Inverse-Normal Method
SAS Macro 6.1: N-Stage Adaptive Designs with Normal Endpoint
SAS Macro 6.2: N-Stage Adaptive Designs with Binary Endpoint
SAS Macro 6.3: N-Stage Adaptive Designs with Various Endpoint
SAS Macro 7.1: Conditional Power
SAS Macro 7.2: Sample-Size Based on Conditional Power
SAS Macro 9.1: Adaptive Design with Sample-Size Reestimation
SAS Macro 11.1: Two-Stage Drop-Loser Adaptive Design
SAS Macro 12.1: Biomarker-Adaptive Design
SAS Macro 14.1: Randomized Play-the-Winner Design
SAS Macro 14.2: Binary Response-Adaptive Randomization
SAS Macro 14.3: Normal Response-Adaptive Randomization
SAS Macro 15.1: 3 + 3 Dose-Escalation Design
SAS Macro 15.2: Continual Reassessment Method
SAS Macro 16.1: Simon Two-Stage Futility Design
SAS Macro A.1: Mixed Exponential Distribution
SAS Macro A.2: Multi-Variate Normal Distribution
List of R Functions
R Function B.1: Sample-Size Based on Conditional Power
R Function B.2: Sample-Size Re-Estimation
R Function B.3: Drop-Loser Design
R Function B.4: Biomarker-Adaptive Design
R Function B.5: Randomized Play-the-Winner Design
R Function B.6: Continual Reassessment Method