Sách Cancer clinical trials current and controversial issues in design and analysis ( PDFDrive com )

Một cuốn sách hay về thử nghiệm lâm sàng trong lĩnh vực ung thư. Sách gồm các phần: 1 Endpoints for Cancer Clinical Trials Stephen L. George, Xiaofei Wang, and Herbert Pang 1.1 Introduction 1.2 Overall Survival 1.3 Endpoints Based on Tumor Measurements 1.3.1 RECIST Criteria 1.3.2 Response Rate as Primary Endpoints 1.3.3 Tumor Response as Continuous Variable 1.4 Progressionfree Survival and Other Composite Endpoints 1.5 Surrogate Endpoints 1.5.1 Definition 1.5.2 Surrogate Endpoint Validation 1.5.3 Remaining Issues 1.6 Patientreported Outcomes 1.6.1 Patientreported Outcomes 1.6.2 Types of PRO for Treatment Comparisons 1.6.3 Health Status, Functional and Symptoms Outcomes 1.6.4 General and Cancerspecific Quality of Life Outcomes 1.6.5 Criteria Used for PRO Instruments Selection 1.6.6 Reliability 1.6.7 Validity1.6.8 Responsiveness of Instruments to Change 1.7 Promising New Approaches 1.7.1 Limitations of Traditional Endpoints 1.7.2 Pharmacokinetic and Pharmacodynamics Responses 1.7.3 Imaging Techniques 1.7.4 Immune Biomarkersbased Endpoints 1.7.5 Criteria for Evaluating Biomarkerbased Endpoints 1.8 Summary 2 Use of Historical Data Simon Wandel, Heinz Schmidli, and Beat Neuenschwander 2.1 Introduction 2.2 Overview of Approaches for Incorporating Historical Data 2.2.1 Introduction 2.2.2 Metaanalytic Approaches 2.2.3 Robust Metaanalyticpredictive Priors and Priordata Conflict 2.2.4 Prior Effective Sample Size 2.3 Applications 2.3.1 Application 1: A Randomized Phase II Trial Using Historical Control Data 2.3.2 Application 2: Design of a Japanese Dose Escalation Study Incorporating Data from Western Patients 2.3.3 Application 3: Noninferiority and Biosimilar Trials 2.4 Discussion 2.5 Appendix 3 Multiplicity Dong Xi, Ekkehard Glimm, and Frank Bretz 3.1 Introduction to Multiplicity Issues 3.1.1 Sources of Multiplicity 3.1.2 Types of Error Rates 3.1.3 Why Multiplicity Adjustment 3.1.4 A Motivating Example 3.2 Common Multiple Comparison Procedures 3.2.1 General Concepts3.2.2 Methods Based on Univariate pValues 3.2.2.1 Methods Based on the Bonferroni Test 3.2.2.2 Methods Based on the Simes Test 3.2.2.3 Numerical Illustration 3.2.3 Parametric Methods 3.2.3.1 Dunnett Test 3.2.3.2 Multiple Testing in Linear Models 3.3 Advanced Multiple Comparison Procedures 3.3.1 Graphical Approaches 3.3.2 Gatekeeping Procedures 3.3.3 Group Sequential Procedures 3.3.3.1 Group Sequential Procedures with Multiple Hypotheses 3.3.3.2 Group Sequential Procedures with a Timetoevent Endpoint 3.3.4 Adaptive Designs 3.4 Applications 3.4.1 Multiple Comparison Procedure in the BELLE2 Trial 3.4.2 Comparison with a Common Control in Timetoevent Trials 3.5 Concluding Remarks 4 Analysis of Safety Data Steven Snapinn and Qi Jiang 4.1 Introduction 4.2 Phase I Clinical Trials 4.2.1 Phase I Designs 4.3 Planning Safety Analyses 4.3.1 Events of Interest 4.3.2 The Statistical Analysis Plan (SAP) 4.3.3 The Program Safety Analysis Plan (PSAP) 4.3.4 Data Monitoring Committee (DMC) 4.4 Safety Signal Detection 4.4.1 Classifying Adverse Events 4.4.2 Statistical Methods for Late Phase Trials 4.4.3 Postmarketing Signal Detection 4.4.4 Singlearm Trials and Combination Studies 4.4.5 Safety Noninferiority Trials4.5 Collecting, Summarizing, and Displaying Safety Data 4.5.1 Data Collection 4.5.2 Reporting Safety Information 4.5.3 Graphical Approaches 4.6 Metaanalysis of Safety Data 4.7 Benefitrisk Analysis 4.8 Summary II Early Phase Clinical Trials 5 Development and Validation of Predictive Signatures Michael C. Sachs and Lisa M. McShane 5.1 Introduction 5.1.1 Prognostic and Predictive Omics Signatures 5.2 Signature Development 5.2.1 Assay Development and Validation 5.2.2 Statistical Development 5.2.3 Iteration and Refinement 5.2.4 Performance Metrics 5.2.5 Estimation of Performance Metrics 5.2.6 Computational Reproducibility 5.2.7 Practical Considerations 5.3 Clinical Utility Assessment 5.3.1 How Omics Signatures Are Used in Clinical Trials 5.3.2 Evaluating Clinical Utility 5.3.3 Power and Sample Size Considerations 5.4 Summary 6 Phase I Trials and Dosefinding Mark R. Conaway and Nolan A. Wages 6.1 Background 6.2 Methods for a Single Cytotoxic Agent 6.2.1 Rulebased Designs 6.2.1.1 The Standard or 3+3 Design 6.2.1.2 Storer’s 2s tage Designs6.2.1.3 Biased Coin Designs 6.2.2 Methods Based on Toxicity Probability Intervals 6.3 Modelbased Methods 6.3.1 The Continual Reassessment Method 6.3.2 Escalation with Overdose Control (EWOC) 6.3.3 EWOC and CRM 6.3.4 Bayesian 2Parameter Logistic Models 6.3.5 Which Method to Use? 6.4 Timetoevent Toxicity Outcomes 6.5 Ordinal Outcomes 6.5.1 Rulebased Methods 6.5.2 Modelbased Methods 6.5.3 Toxicity Scores 6.6 Dose Expansion Cohorts 6.7 Dosefinding Based on Safety and Efficacy 6.8 Combinations of Agents 6.8.1 Assumption of a Single Ordering 6.8.2 Specifying Multiple Possible Orderings 6.8.3 Use of More Flexible Models 6.8.4 Finding Multiple MTDCs 6.9 Patient Heterogeneity 6.10 Noncytotoxic Agents 6.10.1 Locating the OBD 6.11 Summary 7 Design and Analysis of Phase II Cancer Clinical Trials SinHo Jung 7.1 Introduction 7.2 Singlearm Phase II Trials 7.2.1 Optimal Twostage Designs 7.2.2 Estimation of Response Rate 7.2.3 Confidence Interval 7.2.4 pValue Calculation 7.3 Randomized Phase II Trials 7.3.1 Singlestage Design7.3.2 Twostage Design 7.3.2.1 Choice of a1 and a 7.3.2.2 Choice of n1 and n2 7.3.3 Numerical Studies 7.4 Discussion III Late Phase Clinical Trials 8 Sample Size for Survival Trials in Cancer Edward Lakatos 8.1 Introduction 8.2 Departures from Proportionality 8.2.1 Treatment Lag 8.2.2 Treatment Antilag 8.2.3 Both Lag and Antilag 8.2.4 Sample Size Implications 8.2.4.1 Implications for Treatment Lag — Real World Example 8.2.4.2 Exploring the Implications of Treatment Lag and Antilag in a Controlled Setting 8.2.4.3 Sample Size and Power Calculations 8.2.4.4 Treatment Antilag 8.3 Two Paradigms for Which Conventional Wisdom Fails 8.3.1 Eventdriven Trial 8.3.2 Groupsequential Sample Size Inflation Factor 8.4 Sample Size Reestimation and Futility 8.4.1 Estimating the Treatment Effect in a Trial with a Threshold Treatment Lag 8.4.2 Increasing the Sample Size When There Is a Treatment Lag 8.4.3 Interaction between Weighted Statistics and Nonproportional Hazards 8.4.4 Estimating the Treatment Effect in a Trial with a Threshold Treatment Antilag 8.4.5 Sample Size Reestimation in the Presence of Treatment Lag or Antilag: Concluding Remark 8.4.6 Conditional Power, Current Trends, and Nonproportional Hazards 8.5 How the Markov Model Works8.5.1 Introduction 8.5.2 The Exponential Model for Calculating Cumulative Survival Probabilities 8.5.3 The Lifetable Approach to Calculating Cumulative Survival Probabilities 8.5.4 The Markov Model Approach to Calculating Cumulative Survival Probabilities 8.5.4.1 2State Markov Model: At Risk, Failure 8.5.4.2 3State Markov Model: At Risk, Failure, Loss 8.5.4.3 4State Markov Model: At Risk, Failure, Loss, ODIS (Noncompliance) 8.5.5 Using the Markov Model to Calculate Sample Sizes for the Logrank Statistic 8.5.6 Speed and Accuracy 8.6 Discussion and Conclusions 9 Noninferiority Trials Rajeshwari Sridhara and Thomas Gwise 9.1 Introduction 9.2 Endpoint Selection 9.3 Methods for Evaluating the Active Control Effect and Selecting the Noninferiority Margin 9.3.1 Fixed Margin 9.3.2 Synthesis Approach 9.3.3 Bayesian Approach 9.3.4 Placebocontrolled Approach 9.4 Sample Size Determination 9.4.1 Ratio of Proportions 9.4.2 Survival Endpoints 9.5 Interim Monitoring and Analyses 9.6 Multiple Comparisons 9.6.1 Testing of Noninferiority to Superiority and Superiority to Noninferiority 9.7 Missing Data and Noncompliance 9.8 Statistical Inference and Reporting 9.9 Summary10 Quality of Life Diane Fairclough 10.1 Introduction 10.2 Measures of HRQoL 10.3 QOL as an Endpoint in Cancer Trials 10.4 Multiple Endpoints 10.4.1 Summary Measures and Statistics 10.4.2 Multiple Comparisons Adjustments and Gatekeeping Strategies 10.5 Informative Missing Data Due to Dropout 10.5.1 Methods to Be Avoided 10.5.2 Recommended Approach 10.5.3 Sensitivity Analyses 10.5.4 QOL after Death 10.5.5 QALYs and QTWiST 10.5.6 How Much Data Can Be Missing? 10.6 Sample Size or Power Estimation 10.7 Summary IV Personalized Medicine 11 Biomarkerbased Clinical Trials Edward L. Korn and Boris Freidlin 11.1 Introduction 11.2 Analytic Performance of a Biomarker 11.3 Prognostic and Predictive Biomarkers 11.4 Biomarkers in Phase I Trials 11.5 Biomarkers in Phase II Trials 11.5.1 Trials without a Control Arm 11.5.2 Randomized Screening Trials with a Control Arm 11.6 Biomarkers in Phase III Trials 11.6.1 Biomarkers with Compelling Credentials 11.6.2 Biomarkers with Strong Credentials 11.6.2.1 Subgroupspecific Testing Strategies 11.6.2.2 Biomarkerpositive and Overall Strategies 11.6.2.3 Marker Sequential Test Design11.6.2.4 Sample Size Considerations 11.6.3 Biomarkers with Weak Credentials 11.6.4 Interim Monitoring 11.6.5 Retrospective Biomarker Analysis of Phase III Trial Data 11.6.6 Biomarkerstrategy Designs 11.7 Summary 12 Adaptive Clinical Trial Designs in Oncology J. Jack Lee and Lorenzo Trippa 12.1 Introduction 12.2 History of Adaptive Designs 12.3 Bayesian Framework and Its Use in Clinical Trials 12.4 Adaptive Dosefinding Designs for Identifying Optimal Biologic Dose 12.5 Multistage Designs, Group Sequential Designs, Interim Analysis, Early Stopping for Toxicity, Efficacy, or Futility 12.6 Sample Size Reestimation 12.7 Adaptive Randomization, Individual Ethics versus Group Ethics 12.8 Seamless Designs 12.9 Biomarkerguided Adaptive Designs 12.10 Multiarm Adaptive Designs 12.11 Master Protocols, Umbrella Trials, Basket Trials, and Platformbased Designs 12.12 Examples of Trials with Adaptive Designs — Lessons for Design and Conduct 12.13 Software for Adaptive Designs 12.14 Discussion 13 Dynamic Treatment Regimes Marie Davidian, Anastasios (Butch) Tsiatis, and Eric Laber 13.1 Introduction 13.2 Characterization of Treatment Regimes 13.2.1 Decision Rules and Regimes 13.2.2 Classes of Treatment Regimes 13.3 Potential Outcomes Framework 13.3.1 Single Decision 13.3.2 Multiple Decisions13.4 Sequential, Multiple Assignment, Randomized Trials 13.4.1 Data for Studying Dynamic Treatment Regimes 13.4.2 Considerations for SMARTs 13.4.3 Inference on Embedded Regimes in a SMART 13.5 Thinking in Terms of Dynamic Treatment Regimes 13.6 Optimal Treatment Regimes for Personalized Medicine 13.6.1 Characterizing an Optimal Regime 13.6.2 Regressionbased Estimation of an Optimal Regime 13.6.3 Alternative Methods 13.7 Discussion

Cancer Clinical Trials Current and Controversial Issues in Design and Analysis Chapman & Hall/CRC Biostatistics Series Editor-inChief Shein-Chung Chow, Ph.D., Professor, Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina Series Editors Byron Jones, Biometrical Fellow, Statistical Methodology, Integrated Information Sciences, Novartis Pharma AG, Basel, Switzerland Jen-pei Liu, Professor, Division of Biometry, Department of Agronomy, National Taiwan University, Taipei, Taiwan Karl E Peace, Georgia Cancer Coalition, Distinguished Cancer Scholar, Senior Research Scientist and Professor of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia Bruce W Turnbull, Professor, School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York Published Titles Adaptive Design Methods in Clinical Trials, Second Edition Shein-Chung Chow and Mark Chang Adaptive Designs for Sequential Treatment Allocation Alessandro Baldi Antognini and Alessandra Giovagnoli Adaptive Design Theory and Implementation Using SAS and R, Second Edition Mark Chang Advanced Bayesian Methods for Medical Test Accuracy Lyle D Broemeling Advances in Clinical Trial Biostatistics Nancy L Geller Applied Meta-Analysis with R Ding-Geng (Din) Chen and Karl E Peace Basic Statistics and Pharmaceutical Statistical Applications, Second Edition James E De Muth Bayesian Adaptive Methods for Clinical Trials Scott M Berry, Bradley P Carlin, J Jack Lee, and Peter Muller Bayesian Analysis Made Simple: An Excel GUI for WinBUGS Phil Woodward Bayesian Methods for Measures of Agreement Lyle D Broemeling Bayesian Methods for Repeated Measures Lyle D Broemeling Bayesian Methods in Epidemiology Lyle D Broemeling Bayesian Methods in Health Economics Gianluca Baio Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation Ming T Tan, Guo-Liang Tian, and Kai Wang Ng 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randomization designs, 374 design, 84, 91, 188, 367–369, 373, 380, 384, 393, 394 randomization, 367, 373–376, 378, 381, 384, 388–390, 392, 393 treatment strategy, 424 Adverse drug reactions, 106, 107, 109, 110, 113, 115–117, 119, 125 Adverse Event Reporting System (AERS), 116 Adverse Events of Special Interest (AESI), 110, 115, 118, 125 Area Under the Curve (AUC), 311 Basket trials, 367, 385 Bayesian, 16, 38, 39, 41, 42, 46, 48, 50, 57, 92, 108, 109, 115, 145, 172, 174–176, 179, 182, 185, 188, 285, 288, 366–375, 378, 379, 381, 384–387, 389, 391 model averaging, 145, 370, 391 Benefit-risk, 105, 124, 125 Between-trial heterogeneity, 42, 43, 58 Bias model, 38, 39 Biomarker-guided design, 378 biomarker-strategy, 355 biomarker-stratified, 343, 347–351, 353, 355, 356, 358 Biomarkers, 5, 14, 23, 135, 137, 157, 182, 333–337, 341–343, 345, 347, 349, 350, 353, 354, 357, 358, 366, 378, 384, 390, 392 analytical performance, 134, 138–140 assessment, 334, 347, 357 biomarker-negative, 335, 340, 342–344, 346, 347, 349–352, 355 biomarker-positive, 72, 335, 342–347, 349–358 predictive, 5, 133–137, 140, 142–144, 146, 147, 150–157, 188, 334–337, 340, 345, 347, 353, 378, 389, 390 prognostic, 135, 140, 142–144, 147, 150, 152, 157, 334–337, 340, 342, 374, 376, 390, 394 retrospective analysis of, 355 Biosimilar, 22, 24, 39, 53–57 Bonferroni correction, 349 Bonferroni test, 73, 76–79, 82, 84 Breast cancer, 6, 72, 139, 141, 288, 293, 297, 298, 323, 325, 345, 346, 350, 385, 390 adjuvant therapy, 324 ER positive, 141 Cardiotoxicity, 107 Causal inference, 417 Causality determination, 117 Classification and regression trees, 142, 437 Clinical utility, 134, 135, 140, 151, 152, 154–157, 334, 336, 346 Clinical-grade assay, 134 Clustering, 145, 151 Cochrane Handbook, 59 Colorectal cancer, 13, 24, 261, 318, 336, 338, 350 advanced stage, 318 Combination of agents, 341 Common Terminology Criteria for Adverse Events (CTCAE), 114 Confidence interval of response rate, 204, 211 Jennison-Turnbull, 211, 212, 215 Consistency assumption, 420, 424, 425, 428, 433 Consolidated Standards of Reporting Trials (CONSORT), 121 Continual Reassessment Method (CRM), 108, 172, 370 Bayesian, 174, 176, 179 time-to-event, 179, 371 Control group, 42, 45, 48, 57, 58, 79, 80, 94, 111, 116, 117, 119, 121, 242, 247, 262, 263, 269, 271–273, 318 Cross validation, 142–144, 148, 149 Cytostatic drugs, 107 Cytotoxic drugs, 107 Data Monitoring Committee (DMC), 112 Data Safety Monitoring Board (DSMB), 389 Delayed effects, 421, 422 Discounting, 39, 42 Disproportionality analysis, 116 DNA, 107, 133, 137, 386 Dose expansion cohort, 182 Dose-finding, 109, 168, 172, 174, 178, 182–184, 186, 188, 189, 191–193, 367, 370, 371, 390, 391 Dose-limiting toxicity (DLT), 107 Dose-related toxicities, 107 Dunnett test, 73, 78–80, 95 Dynamic treatment regime, 409, 410, 415, 419, 422, 424, 425, 429, 430, 437, 438 decision rules, 413, 414 definition, 410, 413 Early stopping, 75, 218, 354, 367, 371, 373, 375, 376, 379 Efficacy, 4, 5, 14, 50, 53–56, 72, 84, 107, 109, 112, 113, 117, 118, 122, 136, 146, 153, 168, 178, 182–185, 191, 192, 203, 229, 246, 258, 259, 261, 281, 282, 284, 287, 291, 296–298, 300, 301, 310, 341, 366–368, 370–372, 375–379, 381, 384, 389, 391, 438 Empirical Bayes, 370, 394 EndoPredict, 142 Endpoints, 3–9, 12–18, 21–24, 70–72, 75, 81, 83, 85, 86, 88, 89, 93, 115, 117, 123, 179, 192, 283, 284, 292, 297, 300, 308–313, 377, 388 biomarkers-based, 23 composite, 4, 12, 13 disease-free survival (DFS), 12, 17, 283, 324 imaging, 4, 6, 22, 24, 390 immunotherapy, 9, 21, 23, 24 molecular, 9, 14, 137 overall survival (OS), 6, 9, 14, 283 progression-free survival (PFS), 8, 12, 14, 15, 70, 283, 377 surrogate, 4, 6, 12–17, 388 time-to-event (TTE), 284 time-to-progression (TTP), 293 Enrichment design, 92, 153, 156, 343, 345, 346, 353, 356, 357 Equivalent toxicity, 182 Escalation with Overdose Control (EWOC), 175, 391 Ethics, 367, 373, 374, 376, 392, 394 Event-driven trial, 246, 276 Experimental group, 88, 247, 250, 273, 274, 277, 318, 325 Exponential model, 236, 243–245, 248, 249, 261, 262, 266, 269, 270, 273, 276 Feature selection, 140, 143–145, 148 Filtering, 142, 143, 145 Futility, 84, 91, 113, 125, 192, 204, 205, 207, 208, 210, 212, 213, 236, 237, 252, 257–259, 296, 297, 355, 366, 371–373, 375, 378, 379, 386, 387, 389, 392 Gatekeeping, 74, 81, 83, 84, 312–314 Gene expression microarrays, 142, 145 Gibbs sampling, 370 Global Benefit:Risk (GBR) score, 124 Graphical display, 83, 122 Group sequential designs, 83, 84, 93, 153, 276, 367, 371, 372, 391 Group sequential procedure, 84, 85, 88, 237 event-driven analysis, 89 multiple hypotheses, 75, 85, 89 time-to-event endpoint, 6, 84, 88 Hazard rates, 245 constant, 120, 241, 242, 244, 245, 247–249, 271, 276 failure rate, 238, 239, 242–244, 247, 248, 250, 272, 274, 277 non-proportional, 236–238, 249, 250, 252, 258, 260, 276, 277 Historical data, 37–43, 45, 47–49, 52, 53, 55, 57–60, 288–290, 301, 342, 372, 373, 388 Interim analysis, 84–92, 94, 192, 244, 250, 255, 297, 298, 355, 367, 371, 372 Interim monitoring, 296, 354, 355, 371 Inverse probability weighting, 436 Life table approach, 264–266, 273 Linear discriminant analysis, 142 Lung cancer, 6, 10, 12–15, 21, 23, 122, 188, 289, 336, 339, 343, 350, 357, 367, 377, 385, 386, 389 non-small cell, 6, 10, 13–15, 21, 23, 188, 289, 314, 336, 339, 343, 347, 350, 357, 389 Marker Sequential Test (MaST) design, 351 Markov Chain Monte Carlo, 370 Markov model, 237, 241, 242, 244, 245, 247–249, 261, 264–277 at-risk, 263–267, 269–273, 275–277 failure, 242, 244, 248, 250, 261, 263–275, 277, 383 loss, 267, 269 non-compliance, 237, 264, 270–272, 277 Master protocols, 358, 367, 385, 392 Mathematical model, 11, 140 Maximum tolerated dose (MTD), 49, 168, 371 MaxTest, 216, 221, 226–228 Medical Dictionary for Regulatory Activities (MedDRA), 114 Meta-analysis, 16, 38, 42, 57, 58, 106, 122–125, 285–288, 290, 342 individual patient data (IPD), 124 Missing data, 5, 111, 298–300, 308, 311, 314, 315, 317–319, 321, 324, 326, 436, 437 biased methods, 314 joint models, 318 mixture models, 317 sensitivity analysis, 317 shared parameter models, 318 MLE-ordering, 213 Model-based methods, 170, 172, 178, 180, 193 Modified toxicity posterior intervals, 391 Molecular biomarkers, 5 Molecularly targeted agents (MTA), 9, 191, 192 Most successful dose (MSD), 184 Multi-arm adaptive designs, 378 Multi-arm multi-stage, 367, 384 Multi-stage designs, 371, 387 Multiple comparison procedure, 72, 73, 80 Bonferroni test, 73, 76 Dunnett test, 79 gatekeeping procedure, 83, 312 graphical approach, 82, 93, 96 hierarchical test, 81, 84 Hochberg procedure, 73, 77, 297 Holm procedure, 73, 76 Hommel procedure, 74, 77 linear models, 73, 79 multiple endpoints, 310 multiple endpoints, gatekeeing procedures, 312 Simes test, 73, 77 time to event endpoint, 81, 94 Multiple imputation, 319, 321 Multiplicity, 4, 70–73, 75, 76, 86, 92, 95, 96, 111, 113, 115, 123, 297, 308, 372 adjusted p-value, 75 closed test procedure, 73, 76 coherence and consonance, 74 intersection-union test, 73 single-step procedure, 73, 75 stepwise procedure, 73 unadjusted p-value, 75 union-intersection test, 73 NCI Common Terminology Criteria for Adverse Events (NCI CTCAE), 180 No unmeasured confounders assumption, 420, 425, 433 Non-cytotoxic agent, 109, 168, 191, 193 Non-inferiority, 39, 53–55, 70, 113, 117, 118, 281–284, 289, 292, 293, 298, 300, 301 Bayesian approach, 288 endpoint selection, 284 fixed margin, 286 interim monitoring, 296 missing data, 298–300 missing at random (MAR), 299 missing completely at random (MCAR), 299, 314 not missing at random (NMAR), 299 multiple comparisons, 297 non-compliance, 298, 299 non-inferiority margin, 39, 284, 289, 293 reporting, 299, 300 sample size determination, 290 ratio of proportions, 291 survival endpoints, 292 statistical inference, 299 synthesis approach, 287, 288, 290, 296 Number needed to treat (NNT), 124 Oncotype DX, 138, 140, 141 Operating characteristics, 48, 53, 170, 174, 206, 249, 371, 377, 381, 382, 387, 388, 391, 393 Optimal biological dose (OBD), 191, 371 Optimistic bias, 148 Outcome-adaptive randomization, 374, 394 Over-fitting, 134, 148 Overall survival (OS), 6, 9, 14, 17, 70, 283 P-value calculation, 212 Palliative treatments, 321 Patient heterogeneity, 189 Patient-reported outcome (PRO), 18 Brief Pain Inventory, 19 Percent correct selection (PCS), 177 Pharmaceutical Manufacturers of America (PhRMA), 110 Pharmacodynamics (PD), 4, 5, 22, 59 Pharmacokinetics (PK), 5, 182, 370 Pharmacovigilance, 115, 116 post-marketing, 116 Phase I trials, 106, 108, 109, 122, 168, 170, 176, 182, 341, 366, 370 3+3 Design, 108, 169, 170, 370, 391 Phase II trials, 9, 85, 203–205, 208, 212, 215, 216, 218, 219, 226, 229, 341, 342, 345, 371, 386 randomized, 6, 9, 10, 12, 16, 23, 41, 42, 46–49, 57, 58, 85, 151–153, 156, 205, 216, 218, 219, 221, 229, 342, 343, 345, 347, 389, 390 single-arm, 9, 203–205, 226, 341, 342, 371 Phase III trials, 9, 10, 23, 60, 85, 92, 203, 204, 226, 334, 341, 345 Platform-based designs, 367, 385–387 Population drift, 376, 393, 394 Post-marketing, 116 Potential outcomes, 416–420, 427, 432, 433 Power, 14, 38–40, 47–49, 75, 77, 88, 89, 93, 94, 111, 113–115, 117, 152, 156, 157, 174, 175, 177, 187, 190, 205, 217–221, 226–228, 236, 237, 241, 242, 245–250, 252, 258–260, 263, 269, 276, 277, 288, 290–293, 298, 299, 312, 324–326, 345, 353, 354, 356, 367, 373, 374, 376, 378, 380, 384, 393 Predictive Bayesian, 373 Predictive signatures, 133, 143, 154, 157 p > n problem, 142 development of, 335 Prior, 6, 7, 13, 21, 39–53, 57, 83, 108, 110, 138, 143–145, 152, 153, 173–177, 179, 182, 185, 187, 190, 237, 244, 245, 250, 272, 288, 317, 320, 369, 370, 373, 381, 390, 391, 394, 412, 430, 431 commensurate, 39–41 meta-analytic-predictive (MAP), 39, 41 power, 39, 40 robust, 39, 44–46, 48, 52, 53 Prior-data conflict, 45, 46, 48, 49, 52, 60 Prognostic signatures, 140, 144 Program Safety Analysis Plan (PSAP), 106, 111 Progression-free survival (PFS), 8, 12, 14, 15, 70, 283, 340 Propensity score, 421, 431, 437 Prostate cancer, 239, 241, 310, 384, 438 metastatic, 310, 384 Protein, 133, 137, 142, 389 Q-learning, 435–436, 438, 439 Q-TWiST, 124, 309, 321, 323 Qualitative interaction, 135, 140, 144, 147, 156, 157, 335, 336, 339 Quality adjusted life-years (QALYs), 321 Quality of Life (QoL), 4, 5, 9, 18, 19, 21, 135, 308, 310 Activities of Daily Living, 114 endpoint, 308–313, 324 Health-related quality-of-life (HRQoL), 308 measurement, 19 missing data, 308, 311, 319 biased methods, 314 MAR, MCAR, NMAR, 299 mixture models, 317 multiple imputation, 319, 321 sensitivity analysis, 317 shared parameter models, 318 models, 316–318, 321 multiple comparisons, 312 multiple endpoints, 310 gatekeeping procedures, 312 R software, 390 Randomization time, 255, 261 Randomized phase II trial, 9, 10, 41, 47, 205, 216, 217, 219, 229, 342, 345 single-stage design, 216, 218, 221 two-stage Fisher design, 218–221 minimax design, 206, 212, 221 optimal design, 206, 220, 221, 229 Randomized Screening Trials, 343 Recurrence, 323 Regularization, 142–145 Relapse, 323 Reliability, 19, 119, 134, 142, 150 Response rate, 4, 8–10, 15, 21, 47, 48, 55–57, 80, 204, 207, 283, 292–294, 341–343, 374, 375, 377, 381, 426 best overall, 8 complete, 7–9 estimation of, 207 MLE, 207, 208, 210 partial, 7, 204, 411 Response Evaluation Criteria in Solid Tumors (RECIST), 7, 14 UMVUE, 208, 210–213, 215, 226, 229 Response-adaptive, 92, 378, 380, 389 RT-qPCR, 142 Rule-based methods, 170, 178, 180 2-stage designs, 205 3+3 Design, 169, 170 biased coin designs, 171, 172 Safety data, 105, 106, 110–112, 115, 116, 118, 122, 123, 125, 297 Safety Planning, Evaluation and Reporting Team (SPERT), 110 Safety signal detection, 106, 110, 113, 116, 125 Sample size, 5, 39, 42, 46, 47, 49, 59, 75, 84, 91–95, 115, 149, 152, 156, 157, 170, 185, 204–208, 211–213, 215, 216, 218, 220, 221, 226, 236, 237, 241–247, 249, 250, 252, 253, 257–260, 263–265, 269, 271–277, 285, 288, 290–296, 300, 301, 324–326, 345, 353, 356, 366, 367, 372, 373, 375, 377, 378, 380–382, 384, 387, 392, 423, 424, 439 average sample number (ASN), 252 deflation factor, 251 inflation factor, 237, 250, 252 re-estimation, 252, 260, 372 Sarcoma, soft-tissue, 310 Seamless designs, 376, 377 Sensitivity, 23, 59, 116, 118, 122, 125, 139, 282, 289, 299, 301, 316–318, 321, 381–383 Sequential multiple assignment randomized trial (SMART), 410, 419, 429 design considerations, 423, 438 embedded regimes, 423–427, 429, 430, 438 Sequential randomization assumption, 425 Shrinkage, 7–12, 43, 142, 144, 192 Simes test, 73, 76–78 Single-arm phase II trial, 205 admissible designs, 206, 207, 229 minimax design, 206, 212, 221 optimal design, 206, 220, 221, 229 Simon’s two-stage design, 204, 205, 207, 208, 210–212, 218, 229, 371 Software, 38, 42, 60, 75, 149, 150, 188, 189, 241, 242, 247, 272, 285, 292, 319, 326, 368, 389–393, 438 Statistical Analysis Plan (SAP), 106, 110 Subset selection, 142 Summary measures, 311, 312 Surrogate endpoints, 4, 6, 14–18, 388 Time-to-event continual reassessment method (TITE-CRM), 109, 391 Time-to-event toxicity outcomes, 179 Toxicity, 8, 14, 49, 51, 53, 106, 107, 109, 110, 113, 121, 135, 156, 168–193, 203, 302, 309, 315, 318, 323, 336, 341, 357, 366, 367, 370–372, 375, 376, 378, 379, 389–391, 438 side-effects, 277, 323 total toxicity burden, 182 toxicity probability interval (TPI), 171 methods based on TPI, 171, 172, 391 toxicity score, 176, 181 Treatment regime dynamic, 410 Treatment anti-lag, 239, 242, 245–247, 259 Treatment discontinuation, 300, 429 Treatment duration, 431, 432 Treatment effect, 335, 337, 341, 343, 345–347, 349–351, 353, 354 hazard ratio, 4, 85, 120, 122, 123, 146, 149, 154, 156, 239, 243, 244, 246, 253, 259, 260, 271, 284, 293, 295, 336, 337, 343, 383 Treatment lag, 238, 239, 241–245, 253, 257–260, 277 Treatment regime, 6, 58, 75, 135, 357, 410–415, 419, 422, 424–426, 429–432, 437–439 classes of, 414–416, 421 dynamic, 409, 410, 412, 413, 415, 419, 422, 424, 425, 429–432, 437–439 optimal, 410, 415–419, 421, 423, 424, 429, 431–439 policy search estimator, 436 static, 410, 413, 419 value of, 417, 418, 420, 435 value search estimator, 436, 438 Tumor response as continuous variable, 10 TWiST, 124, 323–325 Umbrella trials, 367, 385 UMVUE, 213 Validity, 19–21, 86, 134, 135, 138, 146, 151, 152, 157, 237, 285, 286, 334, 347, 357, 366, 367, 369, 376, 389 construct, 21 content, 20 WinBUGS, 38, 42, 60 ... Sample size for survival trials in cancer (Chapter 8) Non-inferiority trials (Chapter 9) Quality of life (Chapter 1 0) Biomarker-based clinical trials (Chapter 1 1) Adaptive clinical trial designs in oncology (Chapter 1 2) Dynamic treatment regimes (Chapter 1 3). .. Alliance Statistics and Data Center The Alliance for Clinical Trials in Oncology is part of the NCI’s Clinical Trials Network (NCTN) He has been involved in design and analysis of cancer clinical trials and translational studies in the past... Development and validation of predictive signatures (Chapter 5) Phase I trials and dose-finding (Chapter 6) Design and analysis of phase II cancer clinical trials (Chapter 7) Sample size for survival trials in cancer (Chapter 8)