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Innovative Statistics in Regulatory Science [Hardback]

(Duke Univ, USA)
  • Formāts: Hardback, 530 pages, height x width: 234x156 mm, weight: 868 g, 88 Tables, black and white
  • Sērija : Chapman & Hall/CRC Biostatistics Series
  • Izdošanas datums: 07-Nov-2019
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 0367224763
  • ISBN-13: 9780367224769
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Innovative Statistics in Regulatory SciencePalielināt
  • Formāts: Hardback, 530 pages, height x width: 234x156 mm, weight: 868 g, 88 Tables, black and white
  • Sērija : Chapman & Hall/CRC Biostatistics Series
  • Izdošanas datums: 07-Nov-2019
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 0367224763
  • ISBN-13: 9780367224769
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367224769.html
Keywords:
Statistical methods that are commonly used in the review and approval process of regulatory submissions are usually referred to as statistics in regulatory science or regulatory statistics. In a broader sense, statistics in regulatory science can be defined as valid statistics that are employed in the review and approval process of regulatory submissions of pharmaceutical products. In addition, statistics in regulatory science are involved with the development of regulatory policy, guidance, and regulatory critical clinical initiatives related research. This book is devoted to the discussion of statistics in regulatory science for pharmaceutical development. It covers practical issues that are commonly encountered in regulatory science of pharmaceutical research and development including topics related to research activities, review of regulatory submissions, recent critical clinical initiatives, and policy/guidance development in regulatory science.











Devoted entirely to discussing statistics in regulatory science for pharmaceutical development.





Reviews critical issues (e.g., endpoint/margin selection and complex innovative design such as adaptive trial design) in the pharmaceutical development and regulatory approval process.





Clarifies controversial statistical issues (e.g., hypothesis testing versus confidence interval approach, missing data/estimands, multiplicity, and Bayesian design and approach) in review/approval of regulatory submissions.





Proposes innovative thinking regarding study designs and statistical methods (e.g., n-of-1 trial design, adaptive trial design, and probability monitoring procedure for sample size) for rare disease drug development.





Provides insight regarding current regulatory clinical initiatives (e.g., precision/personalized medicine, biomarker-driven target clinical trials, model informed drug development, big data analytics, and real world data/evidence).

This book provides key statistical concepts, innovative designs, and analysis methods that are useful in regulatory science. Also included are some practical, challenging, and controversial issues that are commonly seen in the review and approval process of regulatory submissions.

About the author

Shein-Chung Chow, Ph.D. is currently a Professor at Duke University School of Medicine, Durham, NC. He was previously the Associate Director at the Office of Biostatistics, Center for Drug Evaluation and Research, United States Food and Drug Administration (FDA). Dr. Chow has also held various positions in the pharmaceutical industry such as Vice President at Millennium, Cambridge, MA, Executive Director at Covance, Princeton, NJ, and Director and Department Head at Bristol-Myers

Squibb, Plainsboro, NJ. He was elected Fellow of the American Statistical Association and an elected member of the ISI (International Statistical Institute). Dr. Chow is Editor-in-Chief of the Journal of Biopharmaceutical Statistics and Biostatistics Book Series, Chapman and Hall/CRC Press, Taylor & Francis, New York. Dr. Chow is the author or co-author of over 300 methodology papers and 30 books.
Preface xvii
Author xxi
1 Introduction 1(46)
1.1 Introduction
1(1)
1.2 Key Statistical Concepts
2(20)
1.2.1 Confounding and Interaction
2(6)
1.2.1.1 Confounding
3(3)
1.2.1.2 Interaction
6(2)
1.2.2 Hypotheses Testing and p-values
8(4)
1.2.2.1 Hypotheses Testing
8(2)
1.2.2.2 p-value
10(2)
1.2.3 One-Sided versus Two-Sided Hypotheses
12(2)
1.2.4 Clinical Significance and Clinical Equivalence
14(3)
1.2.5 Reproducibility and Generalizability
17(5)
1.2.5.1 Reproducibility
17(2)
1.2.5.2 Generalizability
19(3)
1.3 Complex Innovative Designs
22(5)
1.3.1 Adaptive Trial Design
22(2)
1.3.1.1 Adaptations
23(1)
1.3.1.2 Types of Adaptive Design
24(1)
1.3.2 The n-of-1 Trial Design
24(2)
1.3.2.1 Complete n-of-1 Trial Design
24(1)
1.3.2.2 Merits and Limitations
25(1)
1.3.3 The Concept of Master Protocols
26(1)
1.3.4 Bayesian Approach
26(1)
1.4 Practical, Challenging, and Controversial Issues
27(16)
1.4.1 Totality-of-the-Evidence
27(1)
1.4.2 (1-α) CI for New Drugs versus (1-2α) CI for Generics/Biosimilars
28(1)
1.4.3 Endpoint Selection
29(1)
1.4.4 Criteria for Decision-Making at Interim
30(1)
1.4.5 Non-inferiority or Equivalence/Similarity Margin Selection
31(1)
1.4.6 Treatment of Missing Data
32(4)
1.4.7 Sample Size Requirement
36(1)
1.4.8 Consistency Test
37(1)
1.4.9 Extrapolation
38(1)
1.4.10 Drug Products with Multiple Components
39(1)
1.4.11 Advisory Committee
40(1)
1.4.12 Recent FDA Critical Clinical Initiatives
41(2)
1.5 Aim and Scope of the Book
43(4)
2 Totality-of-the-Evidence 47(18)
2.1 Introduction
47(1)
2.2 Substantial Evidence
48(1)
2.3 Totality-of-the-Evidence
49(5)
2.3.1 Stepwise Approach
49(2)
2.3.2 Fundamental Biosimilarity Assumptions
51(1)
2.3.3 Examples-Recent Biosimilar Regulatory Submissions
52(1)
2.3.4 Remarks
53(1)
2.4 Practical Issues and Challenges
54(5)
2.4.1 Link among Analytical Similarity, PK/PD Similarity, and Clinical Similarity
54(3)
2.4.2 Totality-of-the-Evidence versus Substantial Evidence
57(1)
2.4.3 Same Regulatory Standards
58(1)
2.5 Development of Totality-of-the-Evidence
59(4)
2.6 Concluding Remarks
63(2)
3 Hypotheses Testing versus Confidence Interval 65(28)
3.1 Introduction
65(1)
3.2 Hypotheses Testing
66(3)
3.2.1 Point Hypotheses Testing
67(1)
3.2.2 Interval Hypotheses Testing
68(1)
3.2.3 Probability of Inconclusiveness
69(1)
3.3 Confidence Interval Approach
69(7)
3.3.1 Confidence Interval Approach with Single Reference
69(1)
3.3.2 Confidence Interval Approach with Multiple References
70(6)
3.3.2.1 Pairwise Comparisons
70(1)
3.3.2.2 Simultaneous Confidence Interval
70(1)
3.3.2.3 Example 1 (False Negative)
71(1)
3.3.2.4 Example 2 (False Positive)
72(4)
3.4 Two One-Sided Tests versus Confidence Interval Approach
76(5)
3.4.1 Two One-Sided Tests (TOST) Procedure
76(2)
3.4.2 Confidence Interval Approach
78(2)
3.4.2.1 Level 1 - α versus Level 1 - 2α
78(1)
3.4.2.2 Significance Level versus Size
79(1)
3.4.2.3 Sizes of Tests Related to Different Confidence Intervals
79(1)
3.4.3 Remarks
80(1)
3.5 A Comparison
81(8)
3.5.1 Performance Characteristics
81(1)
3.5.2 Simulation Studies
82(4)
3.5.3 An Example-Binary Responses
86(3)
3.6 Sample Size Requirement
89(1)
3.7 Concluding Remarks
90(1)
Appendix
91(2)
4 Endpoint Selection 93(30)
4.1 Introduction
93(2)
4.2 Clinical Strategy for Endpoint Selection
95(1)
4.3 Translations among Clinical Endpoints
96(3)
4.4 Comparison of Different Clinical Strategies
99(11)
4.4.1 Test Statistics, Power and Sample Size Determination
99(3)
4.4.2 Determination of the Non-inferiority Margin
102(1)
4.4.3 A Numerical Study
102(8)
4.4.3.1 Absolute Difference versus Relative Difference
103(1)
4.4.3.2 Responders' Rate Based on Absolute Difference
103(1)
4.4.3.3 Responders' Rate Based on Relative Difference
103(7)
4.5 Development of Therapeutic Index Function
110(11)
4.5.1 Introduction
110(4)
4.5.2 Therapeutic Index Function
114(10)
4.5.2.1 Selection of ωi
114(1)
4.5.2.2 Determination of fi(·) and the Distribution of e
115(1)
4.5.2.3 Derivation of Pr(I,|ej) and Pr(ej|Ii)
115(6)
4.6 Concluding Remarks
121(2)
5 Non-inferiority/Equivalence Margin 123(30)
5.1 Introduction
123(1)
5.2 Non-inferiority versus Equivalence
124(3)
5.2.1 Relationship among Non-inferiority, Equivalence, and Superiority
125(1)
5.2.2 Impact on Sample Size Requirement
126(1)
5.3 Non-inferiority Hypothesis
127(3)
5.3.1 Regulatory Requirements
127(1)
5.3.2 Hypothesis Setting and Clinically Meaningful Margin
128(1)
5.3.3 Retention of Treatment Effect in the Absence of Placebo
129(1)
5.4 Methods for Selection of Non-inferiority Margin
130(5)
5.4.1 Classical Method
130(1)
5.4.2 FDA's Recommendations
130(1)
5.4.3 Chow and Shao's Method
131(1)
5.4.4 Alternative Methods
132(1)
5.4.5 An Example
133(2)
5.4.6 Remarks
135(1)
5.5 Strategy for Margin Selection
135(16)
5.5.1 Criteria for Risk Assessment
136(2)
5.5.2 Risk Assessment with Continuous Endpoints
138(5)
5.5.3 Numerical Studies
143(6)
5.5.4 An Example
149(2)
5.6 Concluding Remarks
151(2)
6 Missing Data 153(26)
6.1 Introduction
153(2)
6.2 Missing Data Imputation
155(4)
6.2.1 Last Observation Carried Forward
155(3)
6.2.1.1 Bias-variance Trade-off
156(1)
6.2.1.2 Hypothesis Testing
157(1)
6.2.2 Mean/Median Imputation
158(1)
6.2.3 Regression Imputation
159(1)
6.3 Marginal/Conditional Imputation for Contingency
159(3)
6.3.1 Simple Random Sampling
160(1)
6.3.2 Goodness-of-Fit Test
161(1)
6.4 Test for Independence
162(2)
6.4.1 Results Under Stratified Simple Random Sampling
162(1)
6.4.2 When Number of Strata Is Large
163(1)
6.5 Recent Development
164(12)
6.5.1 Other Methods for Missing Data
164(1)
6.5.2 The Use of Estimand in Missing Data
165(1)
6.5.3 Statistical Methods Under Incomplete Data Structure
166(14)
6.5.3.1 Introduction
166(2)
6.5.3.2 Statistical Methods for 2 x 3 Crossover Designs with Incomplete Data
168(4)
6.5.3.3 A Special Case
172(2)
6.5.3.4 An Example
174(2)
6.6 Concluding Remarks
176(3)
7 Multiplicity 179(16)
7.1 General Concepts
179(1)
7.2 Regulatory Perspective and Controversial Issues
180(2)
7.2.1 Regulatory Perspectives
180(1)
7.2.2 Controversial Issues
181(1)
7.3 Statistical Method for Adjustment of Multiplicity
182(5)
7.3.1 Bonferroni Method
183(1)
7.3.2 Tukey's Multiple Range Testing Procedure
184(1)
7.3.3 Dunnett's Test
184(1)
7.3.4 Closed Testing Procedure
185(1)
7.3.5 Other Tests
186(1)
7.4 Gate-Keeping Procedures
187(5)
7.4.1 Multiple Endpoints
187(1)
7.4.2 Gate-Keeping Testing Procedures
188(4)
7.5 Concluding Remarks
192(3)
8 Sample Size 195(24)
8.1 Introduction
195(1)
8.2 Traditional Sample Size Calculation
196(4)
8.3 Selection of Study Endpoints
200(5)
8.3.1 Translations among Clinical Endpoints
200(2)
8.3.2 Comparison of Different Clinical Strategies
202(3)
8.4 Multiple-stage Adaptive Designs
205(3)
8.5 Sample Size Adjustment with Protocol Amendments
208(3)
8.6 Multi-regional Clinical Trials
211(3)
8.7 Current Issues
214(3)
8.7.1 Is Power Calculation the Only Way?
214(1)
8.7.2 Instability of Sample Size
215(1)
8.7.3 Sample Size Adjustment for Protocol Amendment
216(1)
8.7.4 Sample Size Based on Confidence Interval Approach
216(1)
8.8 Concluding Remarks
217(2)
9 Reproducible Research 219(22)
9.1 Introduction
219(1)
9.2 The Concept of Reproducibility Probability
220(2)
9.3 The Estimated Power Approach
222(6)
9.3.1 Two Samples with Equal Variances
222(3)
9.3.2 Two Samples with Unequal Variances
225(2)
9.3.3 Parallel-Group Designs
227(1)
9.4 Alternative Methods for Evaluation of Reproducibility Probability
228(7)
9.4.1 The Confidence Bound Approach
228(2)
9.4.2 The Bayesian Approach
230(5)
9.5 Applications
235(5)
9.5.1 Substantial Evidence with a Single Trial
235(1)
9.5.2 Sample Size
236(1)
9.5.3 Generalizability between Patient Populations
236(4)
9.6 Future Perspectives
240(1)
10 Extrapolation 241(22)
10.1 Introduction
241(1)
10.2 Shift in Target Patient Population
242(2)
10.3 Assessment of Sensitivity Index
244(9)
10.3.1 The Case Where epsilon Is Random and C Is Fixed
244(3)
10.3.2 The Case Where epsilon Is Fixed and C Is Random
247(3)
10.3.3 The Case Where Both epsilon and C Are Random
250(3)
10.4 Statistical Inference
253(5)
10.4.1 The Case Where epsilon Is Random and C Is Fixed
254(1)
10.4.2 The Case Where epsilon Is Fixed and C Is Random
255(1)
10.4.3 The Case Where epsilon and C Are Random
256(2)
10.5 An Example
258(1)
10.5.1 Case 1: epsilon Is Random and C Is Fixed
258(1)
10.5.2 Case 2: epsilon Is Fixed and C Is Random
259(1)
10.5.3 Case 3: epsilon and C Are Both Random
259(1)
10.6 Concluding Remarks
259(1)
Appendix
260(3)
11 Consistency Evaluation 263(30)
11.1 Introduction
263(1)
11.2 Issues in Multi-regional Clinical Trials
264(2)
11.2.1 Multi-center Trials
264(1)
11.2.2 Multi-regional, Multi-center Trials
265(1)
11.3 Statistical Methods
266(10)
11.3.1 Test for Consistency
266(1)
11.3.2 Assessment of Consistency Index
267(2)
11.3.3 Evaluation of Sensitivity Index
269(1)
11.3.4 Achieving Reproducibility and/or Generalizability
270(3)
11.3.4.1 Specificity Reproducibility Probability for Inequality Test
270(1)
11.3.4.2 Superiority Reproducibility Probability
271(1)
11.3.4.3 Reproducibility Probability Ratio for Inequality Test
272(1)
11.3.4.4 Reproducibility Probability Ratio for Superiority Test
273(1)
11.3.5 Bayesian Approach
273(2)
11.3.6 Japanese Approach
275(1)
11.3.7 The Applicability of Those Approaches
275(1)
11.4 Simulation Study
276(10)
11.4.1 The Case of the Matched-Pair Parallel Design with Normal Data and Superiority Test
276(5)
11.4.2 The Case of the Two-Group Parallel Design with Normal Data and Superiority Test
281(4)
11.4.3 Remarks
285(1)
11.5 An Example
286(4)
11.6 Other Considerations/Discussions
290(1)
11.7 Concluding Remarks
291(2)
12 Drug Products with Multiple Components-Development of TCM 293(48)
12.1 Introduction
293(2)
12.2 Fundamental Differences
295(5)
12.2.1 Medical Theory/Mechanism and Practice
295(2)
12.2.1.1 Medical Practice
296(1)
12.2.2 Techniques of Diagnosis
297(1)
12.2.2.1 Objective versus Subjective Criteria for Evaluability
297(1)
12.2.3 Treatment
298(1)
12.2.3.1 Single Active Ingredient versus Multiple Components
298(1)
12.2.3.2 Fixed Dose versus Flexible Dose
298(1)
12.2.4 Remarks
299(1)
12.3 Basic Considerations
300(4)
12.3.1 Study Design
300(1)
12.3.2 Validation of Quantitative Instrument
301(1)
12.3.3 Clinical Endpoint
302(1)
12.3.4 Matching Placebo
303(1)
12.3.5 Sample Size Calculation
303(1)
12.4 TCM Drug Development
304(27)
12.4.1 Statistical Quality Control Method for Assessing Consistency
304(13)
12.4.1.1 Acceptance Criteria
308(1)
12.4.1.2 Sampling Plan
308(3)
12.4.1.3 Testing Procedure
311(1)
12.4.1.4 Strategy for Statistical Quality Control
311(4)
12.4.1.5 Remarks
315(2)
12.4.2 Stability Analysis
317(6)
12.4.2.1 Models and Assumptions
319(1)
12.4.2.2 Shelf-Life Determination
320(1)
12.4.2.3 An Example
321(2)
12.4.2.4 Discussion
323(1)
12.4.3 Calibration of Study Endpoints in Clinical Development
323(8)
12.4.3.1 Chinese Diagnostic Procedure
324(1)
12.4.3.2 Calibration
325(1)
12.4.3.3 Validity
326(2)
12.4.3.4 Reliability
328(1)
12.4.3.5 Ruggedness
329(2)
12.5 Challenging Issues
331(4)
12.5.1 Regulatory Requirements
331(1)
12.5.2 Test for Consistency
332(1)
12.5.3 Animal Studies
333(1)
12.5.4 Shelf-Life Estimation
333(1)
12.5.5 Indication and Label
334(1)
12.6 Recent Development
335(4)
12.6.1 Introduction
335(1)
12.6.2 Health Index and Efficacy Measure
336(1)
12.6.3 Assessment of Efficacy
336(3)
12.6.4 Remarks
339(1)
12.7 Concluding Remarks
339(2)
13 Adaptive Trial Design 341(26)
13.1 Introduction
341(2)
13.2 What Is Adaptive Design?
343(8)
13.2.1 Adaptations
344(1)
13.2.2 Types of Adaptive Designs
344(8)
13.2.2.1 Adaptive Randomization Design
344(1)
13.2.2.2 Group Sequential Design
345(1)
13.2.2.3 Flexible Sample Size Re-estimation (SSRE) Design
346(1)
13.2.2.4 Drop-the-Losers Design
346(1)
13.2.2.5 Adaptive Dose Finding Design
347(1)
13.2.2.6 Biomarker-Adaptive Design
348(1)
13.2.2.7 Adaptive Treatment-Switching Design
349(1)
13.2.2.8 Adaptive-Hypotheses Design
349(1)
13.2.2.9 Seamless Adaptive Trial Design
350(1)
13.2.2.10 Multiple Adaptive Design
350(1)
13.3 Regulatory/Statistical Perspectives
351(1)
13.4 Impact, Challenges, and Obstacles
352(2)
13.4.1 Impact of Protocol Amendments
352(1)
13.4.2 Challenges in By Design Adaptations
352(2)
13.4.3 Obstacles of Retrospective Adaptations
354(1)
13.5 Some Examples
354(9)
13.6 Strategies for Clinical Development
363(1)
13.7 Concluding Remarks
364(3)
14 Criteria for Dose Selection 367(20)
14.1 Introduction
367(1)
14.2 Dose Selection Criteria
368(3)
14.2.1 Conditional Power
369(1)
14.2.2 Precision Analysis Based on Confidence Interval
370(1)
14.2.3 Predictive Probability of Success
370(1)
14.2.4 Probability of Being the Best Dose
370(1)
14.3 Implementation and Example
371(6)
14.3.1 Single Primary Endpoint
371(1)
14.3.2 Co-primary Endpoints
372(4)
14.3.3 A Numeric Example
376(1)
14.4 Clinical Trial Simulation
377(9)
14.4.1 Single Primary Endpoint
377(1)
14.4.2 Co-primary Endpoints
377(9)
14.5 Concluding Remarks
386(1)
15 Generics and Biosimilars 387(24)
15.1 Introduction
387(1)
15.2 Fundamental Differences
388(1)
15.3 Quantitative Evaluation of Generic Drugs
389(6)
15.3.1 Study Design
390(1)
15.3.2 Statistical Methods
391(1)
15.3.3 Other Criteria for Bioequivalence Assessment
392(3)
15.3.3.1 Population Bioequivalence and Individual Bioequivalence (PBE/IBE)
392(1)
15.3.3.2 Scaled Average Bioequivalence (SABE)
392(1)
15.3.3.3 Scaled Criterion for Drug Interchangeability (SCDI)
393(1)
15.3.3.4 Remarks
394(1)
15.4 Quantitative Evaluation of Biosimilars
395(5)
15.4.1 Regulatory Requirement
395(1)
15.4.2 Biosimilarity
396(2)
15.4.2.1 Basic Principles
396(1)
15.4.2.2 Criteria for Biosimilarity
397(1)
15.4.2.3 Study Design
397(1)
15.4.2.4 Statistical Methods
398(1)
15.4.3 Interchangeability
398(2)
15.4.3.1 Definition and Basic Concepts
398(1)
15.4.3.2 Switching and Alternating
399(1)
15.4.3.3 Study Design
399(1)
15.4.4 Remarks
400(1)
15.5 General Approach for Assessment of Bioequivalence/Biosimilarity
400(4)
15.5.1 Development of Bioequivalence/Biosimilarity Index
400(3)
15.5.2 Remarks
403(1)
15.6 Scientific Factors and Practical Issues for Biosimilars
404(4)
15.6.1 Fundamental Biosimilarity Assumption
404(1)
15.6.2 Endpoint Selection
405(1)
15.6.3 How Similar Is Similar?
405(1)
15.6.4 Guidance on Analytical Similarity Assessment
405(1)
15.6.5 Practical Issues
406(6)
15.6.5.1 Criteria for Biosimilarity (in Terms of Average, Variability, or Distribution)
406(1)
15.6.5.2 Criteria for Interchangeability
407(1)
15.6.5.3 Reference Product Changes
407(1)
15.6.5.4 Extrapolation
407(1)
15.6.5.5 Non-medical Switch
408(1)
15.6.5.6 Bridging Studies for Assessing Biosimilarity
408(1)
15.7 Concluding Remarks
408(3)
16 Precision Medicine 411(22)
16.1 Introduction
411(1)
16.2 The Concept of Precision Medicine
412(3)
16.2.1 Definition of Precision Medicine
412(1)
16.2.2 Biomarker-Driven Clinical Trials
412(2)
16.2.3 Precision Medicine versus Personalized Medicine
414(1)
16.3 Design and Analysis of Precision Medicine
415(8)
16.3.1 Study Designs
415(2)
16.3.2 Statistical Methods
417(5)
16.3.3 Simulation Results
422(1)
16.4 Alternative Enrichment Designs
423(7)
16.4.1 Alternative Designs with/without Molecular Targets
423(2)
16.4.2 Statistical Methods
425(3)
16.4.3 Remarks
428(2)
16.5 Concluding Remarks
430(3)
17 Big Data Analytics 433(24)
17.1 Introduction
433(2)
17.2 Basic Considerations
435(3)
17.2.1 Representativeness of Big Data
435(1)
17.2.2 Selection Bias
435(1)
17.2.3 Heterogeneity
435(1)
17.2.4 Reproducibility and Generalizability
436(1)
17.2.5 Data Quality, Integrity, and Validity
436(1)
17.2.6 FDA Part 11 Compliance
437(1)
17.2.7 Missing Data
437(1)
17.3 Types of Big Data Analytics
438(6)
17.3.1 Case-Control Studies
438(4)
17.3.1.1 Propensity Score Matching
439(1)
17.3.1.2 Model Building
439(2)
17.3.1.3 Model Diagnosis and Validation
441(1)
17.3.1.4 Model Generalizability
441(1)
17.3.2 Meta-analysis
442(4)
17.3.2.1 Issues in Meta-analysis
443(1)
17.4 Bias of Big Data Analytics
444(2)
17.5 Statistical Methods for Estimation of Δ and μp - μN
446(3)
17.5.1 Estimation of Δ
446(2)
17.5.2 Estimation of μp-μN
448(1)
17.5.3 Assumptions and Application
448(1)
17.6 Simulation Study
449(5)
17.7 Concluding Remarks
454(3)
18 Rare Diseases Drug Development 457(26)
18.1 Introduction
457(1)
18.2 Basic Considerations
458(3)
18.2.1 Historical Data
458(1)
18.2.2 Ethical Consideration
459(1)
18.2.3 The Use of Biomarkers
459(1)
18.2.4 Generalizability
460(1)
18.2.5 Sample Size
460(1)
18.3 Innovative Trial Designs
461(5)
18.3.1 n-of-1 Trial Design
461(2)
18.3.1.1 Complete n-of-1 Trial Design
462(1)
18.3.1.2 Merits and Limitations
463(1)
18.3.2 Adaptive Trial Design
463(1)
18.3.3 Other Designs
464(2)
18.3.3.1 Master Protocol
464(2)
18.3.3.2 Bayesian Approach
466(1)
18.4 Statistical Methods for Data Analysis
466(10)
18.4.1 Analysis under a Complete n-of-1 Trial Design
466(4)
18.4.1.1 Statistical Model
466(1)
18.4.1.2 Statistical Analysis
467(1)
18.4.1.3 Sample Size Requirement
468(2)
18.4.2 Analysis under an Adaptive Trial Design
470(6)
18.4.2.1 Two-Stage Adaptive Design
473(3)
18.4.2.2 Remarks
476(1)
18.5 Evaluation of Rare Disease Clinical Trials
476(2)
18.5.1 Predictive Confidence Interval (PCI)
477(1)
18.5.2 Probability of Reproducibility
477(1)
18.6 Some Proposals for Regulatory Consideration
478(4)
18.6.1 Demonstrating Effectiveness or Demonstrating Not Ineffectiveness
478(2)
18.6.2 Two-Stage Adaptive Trial Design for Rare Disease Product Development
480(1)
18.6.3 Probability Monitoring Procedure for Sample Size
481(1)
18.7 Concluding Remarks
482(1)
Bibliography 483(34)
Index 517
Shein-Chung Chow, Ph.D. is currently a Professor at Duke University School of Medicine, Durham, NC. He was previously the Associate Director at the Office of Biostatistics, Center for Drug Evaluation and Research, United States Food and Drug Administration (FDA). Dr. Chow has also held various positions in the pharmaceutical industry such as Vice President at Millennium, Cambridge, MA, Executive Director at Covance, Princeton, NJ, and Director and Department Head at Bristol-Myers

Squibb, Plainsboro, NJ. He was elected Fellow of the American Statistical Association and an elected member of the ISI (International Statistical Institute). Dr. Chow is Editor-in-Chief of the Journal of Biopharmaceutical Statistics and Biostatistics Book Series, Chapman and Hall/CRC Press, Taylor & Francis, New York. Dr. Chow is the author or co-author of over 300 methodology papers and 30 books.