Adaptive design theory and implementation using SAS and R:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
Chapman & Hall/CRC
2008
|
| Schriftenreihe: | Chapman & Hall/CRC biostatistics series
22 |
| Schlagworte: | |
| Online-Zugang: | Table of contents only Publisher description Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references (p. 381-402) and index |
| Beschreibung: | XXII, 418 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9781584889625 |
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| 245 | 1 | 0 | |a Adaptive design theory and implementation using SAS and R |c Mark Chang |
| 264 | 1 | |a Boca Raton [u.a.] |b Chapman & Hall/CRC |c 2008 | |
| 300 | |a XXII, 418 S. |b Ill., graph. Darst. |c 24 cm | ||
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| 490 | 1 | |a Chapman & Hall/CRC biostatistics series |v 22 | |
| 500 | |a Includes bibliographical references (p. 381-402) and index | ||
| 630 | 0 | 4 | |a SAS (Computer file) |
| 650 | 7 | |a Adaptives Verfahren |2 swd | |
| 650 | 7 | |a Computersimulation |2 swd | |
| 650 | 7 | |a Klinisches Experiment |2 swd | |
| 650 | 7 | |a R (Programm) |2 swd | |
| 650 | 7 | |a SAS (Programm) |2 swd | |
| 650 | 4 | |a Clinical trials |x Design | |
| 650 | 4 | |a Clinical trials |x Computer simulation | |
| 650 | 4 | |a Clinical trials |x Statistical methods | |
| 650 | 4 | |a Adaptive sampling (Statistics) | |
| 650 | 4 | |a R (Computer program language) | |
| 650 | 4 | |a Clinical Trials |x methods | |
| 650 | 4 | |a Research Design | |
| 650 | 4 | |a Biometry |x methods | |
| 650 | 4 | |a Data Interpretation, Statistical | |
| 650 | 4 | |a Software | |
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Datensatz im Suchindex
| _version_ | 1804137295023964160 |
|---|---|
| adam_text | IMAGE 1
CONTENTS
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 NONINFCRIORITY 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
IMAGE 2
2.4.1 UNIFIED FORMULATION FOR SAMPLE-SIZE 36
2.4.2 APPLICATION EXAMPLES 38
2.4.3 DETERMINATION OF CONTRAST COEFFICIENTS 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 CONFIDENCE 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
IMAGE 3
5.5.2 LAN-DEMETS ERROR-SPENDING METHOD 115
5.6 FISHER-SHCN 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 MFILLER-SCHAFER 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 CONFIDENCE
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 CONFIDENCE 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
IMAGE 4
9. SAMPLE-SIZE RE-ESTIMATION DESIGN 181
9.1 OPPORTUNITY 181
9.2 ADAPTATION RULES 182
9.2.1 ADJUSTMENT BASED ON EFFECT 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-VALUC 192
9.5.2 CONFIDENCE 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
10.1 MULTIPLICITY 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
10.2 MULTIPLE-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
11.2 METHOD 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
11.3 METHOD 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
IMAGE 5
11.5 SUMMARY AND DISCUSSION 236
12. BIOMARKER-ADAPTIVE DESIGN 239
12.1 OPPORTUNITIES 239
12.2 DESIGN WITH CLASSIFIER BIOMARKER 241
12.2.1 SETTING THE SCENE 241
12.2.2 CLASSIC DESIGN WITH CLASSIFIER BIOMARKER 243 12.2.3 ADAPTIVE
DESIGN WITH CLASSIFIER 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
13.2 MIXED EXPONENTIAL SURVIVAL MODEL 260
13.2.1 MIXED EXPONENTIAL MODEL 260
13.2.2 EFFECT 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
IMAGE 6
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. BAYCSIAN ADAPTIVE DESIGN 307
16.1 INTRODUCTION 307
16.2 BAYESIAN LEARNING MECHANISM 308
16.3 BAYCSIAN BASICS 309
16.3.1 BAYCS 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
17.3 WORKING WITH REGULATORY AGENCY 332
17.4 TRIAL MONITORING 333
IMAGE 7
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
18.1 MY 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 SUFFICIENCY PRINCIPLE 352
18.5.2 MINIMUM SUFFICIENCY PRINCIPLE AND EFFICIENCY . . .. 353 18.5.3
CONDITIONALLY 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.I 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
|
| adam_txt |
IMAGE 1
CONTENTS
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 NONINFCRIORITY 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
IMAGE 2
2.4.1 UNIFIED FORMULATION FOR SAMPLE-SIZE 36
2.4.2 APPLICATION EXAMPLES 38
2.4.3 DETERMINATION OF CONTRAST COEFFICIENTS 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 CONFIDENCE 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
IMAGE 3
5.5.2 LAN-DEMETS ERROR-SPENDING METHOD 115
5.6 FISHER-SHCN 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 MFILLER-SCHAFER 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 CONFIDENCE
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 CONFIDENCE 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
IMAGE 4
9. SAMPLE-SIZE RE-ESTIMATION DESIGN 181
9.1 OPPORTUNITY 181
9.2 ADAPTATION RULES 182
9.2.1 ADJUSTMENT BASED ON EFFECT 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-VALUC 192
9.5.2 CONFIDENCE 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
10.1 MULTIPLICITY 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
10.2 MULTIPLE-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
11.2 METHOD 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
11.3 METHOD 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
IMAGE 5
11.5 SUMMARY AND DISCUSSION 236
12. BIOMARKER-ADAPTIVE DESIGN 239
12.1 OPPORTUNITIES 239
12.2 DESIGN WITH CLASSIFIER BIOMARKER 241
12.2.1 SETTING THE SCENE 241
12.2.2 CLASSIC DESIGN WITH CLASSIFIER BIOMARKER 243 12.2.3 ADAPTIVE
DESIGN WITH CLASSIFIER 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
13.2 MIXED EXPONENTIAL SURVIVAL MODEL 260
13.2.1 MIXED EXPONENTIAL MODEL 260
13.2.2 EFFECT 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
IMAGE 6
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. BAYCSIAN ADAPTIVE DESIGN 307
16.1 INTRODUCTION 307
16.2 BAYESIAN LEARNING MECHANISM 308
16.3 BAYCSIAN BASICS 309
16.3.1 BAYCS' 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
17.3 WORKING WITH REGULATORY AGENCY 332
17.4 TRIAL MONITORING 333
IMAGE 7
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
18.1 MY 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 SUFFICIENCY PRINCIPLE 352
18.5.2 MINIMUM SUFFICIENCY PRINCIPLE AND EFFICIENCY . . . 353 18.5.3
CONDITIONALLY 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.I 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 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Chang, Mark 1955- |
| author_GND | (DE-588)1044405805 |
| author_facet | Chang, Mark 1955- |
| author_role | aut |
| author_sort | Chang, Mark 1955- |
| author_variant | m c mc |
| building | Verbundindex |
| bvnumber | BV023058663 |
| callnumber-first | R - Medicine |
| callnumber-label | R853 |
| callnumber-raw | R853.C55 |
| callnumber-search | R853.C55 |
| callnumber-sort | R 3853 C55 |
| callnumber-subject | R - General Medicine |
| ctrlnum | (OCoLC)86113130 (DE-599)BVBBV023058663 |
| dewey-full | 610.72/4 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 610 - Medicine and health |
| dewey-raw | 610.72/4 |
| dewey-search | 610.72/4 |
| dewey-sort | 3610.72 14 |
| dewey-tens | 610 - Medicine and health |
| discipline | Medizin |
| discipline_str_mv | Medizin |
| format | Book |
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| id | DE-604.BV023058663 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:27:50Z |
| indexdate | 2024-07-09T21:10:01Z |
| institution | BVB |
| isbn | 9781584889625 |
| language | English |
| lccn | 2007011412 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016261929 |
| oclc_num | 86113130 |
| open_access_boolean | |
| owner | DE-29 DE-19 DE-BY-UBM DE-11 DE-578 |
| owner_facet | DE-29 DE-19 DE-BY-UBM DE-11 DE-578 |
| physical | XXII, 418 S. Ill., graph. Darst. 24 cm |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Chapman & Hall/CRC |
| record_format | marc |
| series | Chapman & Hall/CRC biostatistics series |
| series2 | Chapman & Hall/CRC biostatistics series |
| spelling | Chang, Mark 1955- Verfasser (DE-588)1044405805 aut Adaptive design theory and implementation using SAS and R Mark Chang Boca Raton [u.a.] Chapman & Hall/CRC 2008 XXII, 418 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC biostatistics series 22 Includes bibliographical references (p. 381-402) and index SAS (Computer file) Adaptives Verfahren swd Computersimulation swd Klinisches Experiment swd R (Programm) swd SAS (Programm) swd Clinical trials Design Clinical trials Computer simulation Clinical trials Statistical methods Adaptive sampling (Statistics) R (Computer program language) Clinical Trials methods Research Design Biometry methods Data Interpretation, Statistical Software Biostatistik (DE-588)4729990-3 gnd rswk-swf SAS Programm (DE-588)4195685-0 gnd rswk-swf Adaptives Verfahren (DE-588)4310560-9 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Biostatistik (DE-588)4729990-3 s Adaptives Verfahren (DE-588)4310560-9 s 1\p DE-604 R Programm (DE-588)4705956-4 s 2\p DE-604 SAS Programm (DE-588)4195685-0 s 3\p DE-604 Chapman & Hall/CRC biostatistics series 22 (DE-604)BV023097394 22 http://www.loc.gov/catdir/toc/ecip0713/2007011412.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0801/2007011412-d.html Publisher description SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016261929&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Chang, Mark 1955- Adaptive design theory and implementation using SAS and R Chapman & Hall/CRC biostatistics series SAS (Computer file) Adaptives Verfahren swd Computersimulation swd Klinisches Experiment swd R (Programm) swd SAS (Programm) swd Clinical trials Design Clinical trials Computer simulation Clinical trials Statistical methods Adaptive sampling (Statistics) R (Computer program language) Clinical Trials methods Research Design Biometry methods Data Interpretation, Statistical Software Biostatistik (DE-588)4729990-3 gnd SAS Programm (DE-588)4195685-0 gnd Adaptives Verfahren (DE-588)4310560-9 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4729990-3 (DE-588)4195685-0 (DE-588)4310560-9 (DE-588)4705956-4 |
| title | Adaptive design theory and implementation using SAS and R |
| title_auth | Adaptive design theory and implementation using SAS and R |
| title_exact_search | Adaptive design theory and implementation using SAS and R |
| title_exact_search_txtP | Adaptive design theory and implementation using SAS and R |
| title_full | Adaptive design theory and implementation using SAS and R Mark Chang |
| title_fullStr | Adaptive design theory and implementation using SAS and R Mark Chang |
| title_full_unstemmed | Adaptive design theory and implementation using SAS and R Mark Chang |
| title_short | Adaptive design theory and implementation using SAS and R |
| title_sort | adaptive design theory and implementation using sas and r |
| topic | SAS (Computer file) Adaptives Verfahren swd Computersimulation swd Klinisches Experiment swd R (Programm) swd SAS (Programm) swd Clinical trials Design Clinical trials Computer simulation Clinical trials Statistical methods Adaptive sampling (Statistics) R (Computer program language) Clinical Trials methods Research Design Biometry methods Data Interpretation, Statistical Software Biostatistik (DE-588)4729990-3 gnd SAS Programm (DE-588)4195685-0 gnd Adaptives Verfahren (DE-588)4310560-9 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | SAS (Computer file) Adaptives Verfahren Computersimulation Klinisches Experiment R (Programm) SAS (Programm) Clinical trials Design Clinical trials Computer simulation Clinical trials Statistical methods Adaptive sampling (Statistics) R (Computer program language) Clinical Trials methods Research Design Biometry methods Data Interpretation, Statistical Software Biostatistik SAS Programm R Programm |
| url | http://www.loc.gov/catdir/toc/ecip0713/2007011412.html http://www.loc.gov/catdir/enhancements/fy0801/2007011412-d.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016261929&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023097394 |
| work_keys_str_mv | AT changmark adaptivedesigntheoryandimplementationusingsasandr |