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El. knyga: Controversial Statistical Issues in Clinical Trials

(Duke Univ, USA)
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"Preface In pharmaceutical/clinical development of a test drug or treatment, relevant clinical data are usually collected from subjects with the diseases under study in order to evaluate safety and efficacy of the test drug or treatment under investigation. To provide accurate and reliable assessment, well-controlled clinical trials under valid study design are necessarily conducted. Clinical trial process is a lengthy and costly process, which is necessary to ensure a fair and reliable assessment of thetest treatment under investigation. Clinical trial process consists of protocol development, trial conduct, data collection, statistical analysis/interpretation, and reporting. In practice, controversial issues evitably occur regardless the compliance ofgood statistical practice (GSP) and good clinical practice (GCP). Controversial issues in clinical trials are referred to as debatable issues that are commonly encountered during the conduct of clinical trials. In practice, controversial issues could be raised from, but are not limited to, (1) compromises between theoretical and real/common practices, (2) miscommunication and/or misunderstanding in perception/interpretation among regulatory agencies, clinical scientists, and biostatisticians, and (3) disagreement, inconsistency, miscommunication/misunderstanding, and errors in clinical practice. "--Provided by publisher.

Recenzijos

" it is very valuable that the book calls the readers attention to the numerous problems which are relevant every day and I recommend to make this book available to personnel in all biometrical university departments, CROs and operating departments in the pharmaceutical industry to enable them to cope with the most important controversial aspects in planning and execution of clinical studies." Rainer Muche, ISCB News, December 2013

"This volume deals with an important areaissues in clinical trials research which are perhaps not fully resolved. it is wide ranging, covering all aspects of clinical trials, and has excellent links and references to regulatory aspects. It will therefore provide a useful reference work for clinical trials researchers." David J. Hand, International Statistical Review, 2012

"I would recommend this book since it covers a number of areas that have not been covered in as much detail elsewhere. In particular, I thought the chapters on molecularly targeted therapies, follow-on biologics, multiregional clinical trials and good statistical practices were well written and useful." William Mietlowski, Journal of Biopharmaceutical Statistics, Issue 5, 2012

"Written by one of the preeminent experts in the field, this book provides a useful desk reference and state-of-the art examination of problematic issues in clinical trials for scientists in the pharmaceutical industry, medical/statistical reviewers in government regulatory agencies, and researchers and students in academia." Zentralblatt MATH

Preface xvii
1 Introduction
1(16)
1.1 Introduction
1(1)
1.2 Pharmaceutical Development
2(5)
1.2.1 Nonclinical Development
3(1)
1.2.2 Preclinical Development
4(1)
1.2.3 Clinical Development
5(2)
1.3 Controversial Issues
7(7)
1.4 Aim and Structure of the Book
14(3)
2 Good Statistical Practices
17(14)
2.1 Introduction
17(2)
2.2 Statistical Principles
19(7)
2.2.1 Bias and Variability
19(1)
2.2.2 Confounding and Interaction
19(1)
2.2.3 Hypotheses Testing
20(3)
2.2.4 Type I Error and Power
23(1)
2.2.5 Randomization
24(1)
2.2.6 Sample Size Determination/Justification
24(1)
2.2.7 Statistical Difference and Scientific Difference
25(1)
2.2.8 One-Sided Test versus Two-Sided Test
25(1)
2.3 Good Statistical Practices in Europe
26(2)
2.4 Implementation of GSP
28(2)
2.5 Concluding Remarks
30(1)
3 Bench-to-Bedside Translational Research
31(20)
3.1 Introduction
31(1)
3.2 Biomarker Development
32(5)
3.2.1 Optimal Variable Screening
33(2)
3.2.2 Model Selection and Validation
35(1)
3.2.3 Remarks
36(1)
3.3 One-Way/Two-Way Translational Process
37(9)
3.3.1 One-Way Translational Process
38(6)
3.3.2 Two-Way Translational Process
44(2)
3.4 Lost in Translation
46(1)
3.5 Animal Model versus Human Model
47(2)
3.6 Concluding Remarks
49(2)
4 Bioavailability and Bioequivalence
51(18)
4.1 Introduction
51(1)
4.2 Bioequivalence Assessment
52(2)
4.2.1 Study Design
52(1)
4.2.2 Statistical Methods
53(1)
4.2.3 Remarks
54(1)
4.3 Drug Interchangeability
54(3)
4.3.1 Drug Prescribability and Drug Switchability
55(1)
4.3.2 Population and Individual Bioequivalence
55(2)
4.4 Controversial Issues
57(5)
4.4.1 Fundamental Bioequivalence Assumption
57(1)
4.4.2 One-Fits-All Criterion
58(1)
4.4.3 Issues Related to Log Transformation
59(3)
4.5 Frequently Asked Questions
62(4)
4.5.1 What If We Pass Raw Data Model but Fail Log-Transformed Data Model?
62(1)
4.5.2 What If We Pass AUC but Fail Cmax?
63(1)
4.5.3 What If We Fail by a Relatively Small Margin?
63(1)
4.5.4 Can We Still Assess Bioequivalence If There Is a Significant Sequence Effect?
64(1)
4.5.5 What Should We Do When We Have Almost Identical Means but Still Fail to Meet the Bioequivalence Criterion?
64(1)
4.5.6 Power and Sample Size Calculation Based on Raw Data Model and Log-Transformed Model Are Different
65(1)
4.5.7 Adjustment for Multiplicity
65(1)
4.6 Concluding Remarks
66(3)
5 Hypotheses for Clinical Evaluation and Significant Digits
69(22)
5.1 Introduction
69(5)
5.2 Hypotheses for Clinical Evaluation
74(1)
5.3 Statistical Methods for Testing Composite Hypotheses of NS
75(3)
5.4 Impact on Power and Sample Size Calculation
78(4)
5.4.1 Fixed Power Approach
78(2)
5.4.2 Fixed Sample Size Approach
80(1)
5.4.3 Remarks
81(1)
5.5 Significant Digits
82(6)
5.5.1 Chow's Proposal
83(1)
5.5.2 Statistical Justification
84(4)
5.6 Concluding Remarks
88(3)
6 Instability of Sample Size Calculation
91(16)
6.1 Introduction
91(1)
6.2 Sample Size Calculation
92(1)
6.3 Instability and Bootstrap-Median Approach
93(4)
6.3.1 Instability of Sample Size Calculation
93(4)
6.3.2 The Bootstrap-Median Approach
97(1)
6.4 Simulation Study
97(5)
6.4.1 One-Sample Problem
97(5)
6.4.2 Two-Sample Problem
102(1)
6.5 An Example
102(3)
6.6 Concluding Remarks
105(2)
7 Integrity of Randomization/Blinding
107(28)
7.1 Introduction
107(1)
7.2 The Effect of Mix-Up Randomization
108(3)
7.3 Blocking Size in Randomization
111(13)
7.3.1 Probability of Correctly Guessing
112(3)
7.3.2 Numerical Study
115(9)
7.3.3 Remarks
124(1)
7.4 Test for Integrity of Blinding
124(2)
7.5 Analysis under Breached Blindness
126(4)
7.6 An Example
130(4)
7.7 Concluding Remarks
134(1)
8 Clinical Strategy for Endpoint Selection
135(18)
8.1 Introduction
135(2)
8.2 Clinical Strategy for Endpoint Selection
137(1)
8.3 Translations among Clinical Endpoints
138(3)
8.4 Comparison of Different Clinical Strategies
141(3)
8.4.1 Test Statistics, Power, and Sample Size Determination
141(2)
8.4.2 Determination of the Non-Inferiority Margin
143(1)
8.5 A Numerical Study
144(3)
8.5.1 Absolute Difference versus Relative Difference
144(3)
8.5.2 Responders' Rate Based on Absolute Difference
147(1)
8.5.3 Responders' Rate Based on Relative Difference
147(1)
8.6 Concluding Remarks
147(6)
9 Protocol Amendments
153(24)
9.1 Introduction
153(1)
9.2 Moving Target Patient Population
154(2)
9.3 Analysis with Covariate Adjustment
156(7)
9.3.1 Continuous Study Endpoint
156(2)
9.3.2 Binary Response
158(5)
9.4 Assessment of Sensitivity Index
163(8)
9.4.1 The Case Where ε Is Random and C Is Fixed
164(2)
9.4.2 The Case Where ε Is Fixed and C Is Random
166(5)
9.5 Sample Size Adjustment
171(1)
9.6 Concluding Remarks
172(5)
10 Seamless Adaptive Trial Designs
177(26)
10.1 Introduction
177(1)
10.2 Controversial Issues
178(4)
10.2.1 Flexibility and Efficiency
179(1)
10.2.2 Validity and Integrity
179(2)
10.2.3 Regulatory Concerns
181(1)
10.3 Types of Two-Stage Seamless Adaptive Designs
182(1)
10.4 Analysis for Seamless Design with Same Study Objectives/Endpoints
183(9)
10.4.1 Theoretical Framework
184(2)
10.4.2 Two-Stage Adaptive Design
186(4)
10.4.3 Conditional Power
190(2)
10.5 Analysis for Seamless Design with Different Endpoints
192(4)
10.6 Analysis for Seamless Design with Different Objectives/Endpoints
196(5)
10.6.1 Nonadaptive Version
196(2)
10.6.2 Adaptive Version
198(1)
10.6.3 An Example
199(2)
10.7 Concluding Remarks
201(2)
11 Multiplicity in Clinical Trials
203(14)
11.1 General Concept
203(1)
11.2 Regulatory Perspective and Controversial Issues
204(2)
11.2.1 Regulatory Perspectives
204(1)
11.2.2 Controversial Issues
205(1)
11.3 Statistical Method for Adjustment of Multiplicity
206(5)
11.3.1 Bonferroni's Method
206(1)
11.3.2 Tukey's Multiple Range Testing Procedure
207(1)
11.3.3 Dunnett's Test
208(1)
11.3.4 Closed Testing Procedure
209(1)
11.3.5 Other Tests
210(1)
11.4 Gatekeeping Procedures
211(4)
11.4.1 Multiple Endpoints
211(1)
11.4.2 Gatekeeping Testing Procedures
212(3)
11.5 Concluding Remarks
215(2)
12 Independence of Data Monitoring Committee
217(16)
12.1 Introduction
217(1)
12.2 Regulatory Requirements
218(2)
12.2.1 Determining Need for a DMC
219(1)
12.2.2 Confidentiality of Interim Data and Analysis
219(1)
12.2.3 Desirability of an Independent DMC
220(1)
12.3 DMC Composition and Charter
220(2)
12.3.1 DMC Composition and Support Staff
221(1)
12.3.2 DMC Charter
221(1)
12.4 DMC's Functions and Activities
222(5)
12.4.1 Randomization
222(1)
12.4.2 Critical Data Flow
223(1)
12.4.3 DMC Report and Analysis Plan
223(1)
12.4.4 Sensitivity Analysis
224(1)
12.4.5 Executive Summary/Report
224(1)
12.4.6 DMC Meetings
225(1)
12.4.7 DMC Documents and Information Dissemination
226(1)
12.4.8 DMC Recommendations
226(1)
12.4.9 DMC Organizational Flow
226(1)
12.5 Independence of DMC
227(3)
12.5.1 Some Observations
228(1)
12.5.2 Controversial Issues
229(1)
12.6 Concluding Remarks
230(3)
13 Two-Way ANOVA versus One-Way ANOVA with Repeated Measures
233(18)
13.1 Introduction
233(1)
13.2 One-Way ANOVA with Repeated Measures
234(2)
13.3 Two-Way ANOVA
236(1)
13.4 Statistical Evaluation
237(3)
13.5 Simulation Study
240(4)
13.6 An Example
244(1)
13.7 Discussion
245(6)
14 Validation of QOL Instruments
251(24)
14.1 Introduction
251(2)
14.2 QOL Assessment
253(1)
14.3 Performance Characteristics
254(4)
14.3.1 Validity
254(2)
14.3.2 Reliability
256(1)
14.3.3 Reproducibility
257(1)
14.4 Responsiveness and Sensitivity
258(7)
14.4.1 Statistical Model
259(2)
14.4.2 Precision Index
261(1)
14.4.3 Power Index
262(2)
14.4.4 Sample Size Determination
264(1)
14.5 Utility Analysis and Calibration
265(2)
14.5.1 Utility Analysis
265(1)
14.5.2 Calibration
266(1)
14.6 Analysis of Parallel Questionnaire
267(4)
14.7 An Example
271(2)
14.8 Concluding Remarks
273(2)
15 Missing Data Imputation
275(16)
15.1 Introduction
275(2)
15.2 Last Observation Carry Forward
277(3)
15.2.1 Bias-Variance Trade-Off
278(1)
15.2.2 Hypothesis Testing
279(1)
15.3 Mean/Median Imputation
280(1)
15.4 Regression Imputation
281(1)
15.5 Marginal/Conditional Imputation for Contingency
281(3)
15.5.1 Simple Random Sampling
282(1)
15.5.2 Goodness-of-Fit Test
283(1)
15.6 Testing for Independence
284(2)
15.6.1 Results under Stratified Simple Random Sampling
285(1)
15.6.2 When Number of Strata Is Large
285(1)
15.7 Controversial Issues
286(1)
15.8 Recent Development
287(2)
15.9 Concluding Remarks
289(2)
16 Center Grouping
291(12)
16.1 Introduction
291(1)
16.2 Selection of the Number of Centers
292(1)
16.3 Impact of Treatment Imbalance on Power
293(1)
16.4 Center Grouping
294(5)
16.5 Procedure for Center Grouping
299(2)
16.6 An Example
301(2)
17 Non-Inferiority Margin
303(30)
17.1 Introduction
303(1)
17.2 Non-Inferiority Margin
304(5)
17.3 Statistical Test Based on Treatment Difference
309(6)
17.3.1 Tests Based on Historical Data under Constancy Condition
310(2)
17.3.2 Constancy Condition
312(1)
17.3.3 Tests without Historical Data
312(1)
17.3.4 An Example
313(2)
17.4 Statistical Tests Based on Relative Risk
315(6)
17.4.1 Hypotheses for Non-Inferiority Margin
316(1)
17.4.2 Tests Based on Historical Data under Constancy Condition
317(2)
17.4.3 Tests without Historical Data
319(1)
17.4.4 An Example
320(1)
17.5 Mixed Non-Inferiority Margin
321(5)
17.5.1 Hypotheses for Mixed Non-Inferiority Margin
321(1)
17.5.2 Non-Inferiority Tests
322(4)
17.5.3 An Example
326(1)
17.6 Recent Developments
326(2)
17.6.1 A Special Issue of the Journal of Biopharmaceutical Statistics
326(1)
17.6.2 FDA Draft Guidance
327(1)
17.7 Concluding Remarks
328(5)
18 QT Studies with Recording Replicates
333(20)
18.1 Introduction
333(2)
18.2 Study Designs and Models
335(2)
18.3 Power and Sample Size Calculation
337(5)
18.3.1 Parallel-Group Design
337(1)
18.3.2 Crossover Design
338(1)
18.3.3 Remarks
339(3)
18.4 Adjustment for Covariates
342(2)
18.4.1 Parallel-Group Design
342(1)
18.4.2 Crossover Design
343(1)
18.5 Optimization for Sample Size Allocation
344(1)
18.6 Test for QT/QTc Prolongation
345(4)
18.6.1 Parallel-Group Design
345(2)
18.6.2 Crossover Design
347(1)
18.6.3 Numerical Study
348(1)
18.7 Recent Developments
349(1)
18.8 Concluding Remarks
350(3)
19 Multiregional Clinical Trials
353(28)
19.1 Introduction
353(1)
19.2 Multiregional (Multinational), Multicenter Trials
354(6)
19.2.1 Multicenter Trials
354(3)
19.2.2 Multiregional (Multinational), Multicenter Trials
357(3)
19.3 Selection of the Number of Sites
360(8)
19.3.1 Two-Stage Sampling
361(2)
19.3.2 Testing Procedure
363(1)
19.3.3 Optimal Selection
364(3)
19.3.4 An Example
367(1)
19.4 Sample Size Calculation and Allocation
368(7)
19.4.1 Some Background
368(2)
19.4.2 Proposals of Statistical Guidance---Asian Perspective
370(5)
19.5 Statistical Methods for Bridging Studies
375(4)
19.5.1 Test for Consistency
377(1)
19.5.2 Test for Reproducibility and Generalizability
377(1)
19.5.3 Test for Similarity
378(1)
19.6 Concluding Remarks
379(2)
20 Dose Escalation Trials
381(14)
20.1 Introduction
381(2)
20.2 Traditional Escalation Rule
383(1)
20.3 Continual Reassessment Method
383(4)
20.3.1 Implementation of CRM
384(1)
20.3.2 CRM in Conjunction with Bayesian Approach
385(2)
20.3.3 Extended CRM Trial Design
387(1)
20.4 Design Selection and Sample Size
387(5)
20.4.1 Criteria for Design Selection
387(1)
20.4.2 Sample Size Justification
388(4)
20.5 Concluding Remarks
392(3)
21 Enrichment Process in Target Clinical Trials
395(26)
21.1 Introduction
395(1)
21.2 Identification of Differentially Expressed Genes
396(4)
21.3 Optimal Representation of in Vitro Diagnostic Multivariate Index Assays
400(2)
21.4 Validation of in Vitro Diagnostic Multivariate Index Assays
402(3)
21.5 Enrichment Process
405(2)
21.6 Study Designs of Target Clinical Trials
407(4)
21.7 Analysis of Target Clinical Trials
411(7)
21.8 Discussion
418(3)
22 Clinical Trial Simulation
421(22)
22.1 Introduction
421(1)
22.2 Process for Clinical Trial Simulation
422(4)
22.2.1 Model and Assumptions
422(1)
22.2.2 Performance Characteristics
423(1)
22.2.3 An Example
424(1)
22.2.4 Remarks
425(1)
22.3 EM Algorithm
426(4)
22.3.1 General Description
426(1)
22.3.2 An Example
427(3)
22.4 Resampling Method: Bootstrapping
430(1)
22.4.1 General Description
430(1)
22.4.2 Types of Bootstrap Scheme
430(1)
22.4.3 Methods for Bootstrap Confidence Intervals
431(1)
22.5 Clinical Applications
431(9)
22.5.1 Target Clinical Trials with Enrichment Designs
431(1)
22.5.2 Dose Escalation Trials
432(8)
22.6 Concluding Remarks
440(3)
23 Traditional Chinese Medicine
443(44)
23.1 Introduction
443(1)
23.2 Fundamental Differences
444(6)
23.2.1 Medical Theory/Mechanism and Practice
445(1)
23.2.2 Medical Practice
446(1)
23.2.3 Techniques of Diagnosis
446(1)
23.2.4 Treatment
447(2)
23.2.5 Remarks
449(1)
23.3 Basic Considerations
450(4)
23.3.1 Study Design
450(1)
23.3.2 Validation of Quantitative Instrument
451(1)
23.3.3 Clinical Endpoint
452(1)
23.3.4 Matching Placebo
452(1)
23.3.5 Sample Size Calculation
453(1)
23.4 Controversial Issues
454(3)
23.4.1 Test for Consistency
454(1)
23.4.2 Animal Studies
455(1)
23.4.3 Stability Analysis
455(1)
23.4.4 Regulatory Requirements
456(1)
23.4.5 Indication and Label
456(1)
23.5 Recent Development
457(27)
23.5.1 Statistical Quality Control Method for Assessing Consistency
457(13)
23.5.2 Stability Analysis for TCM
470(6)
23.5.3 Calibration of Study Endpoints
476(8)
23.6 Concluding Remarks
484(3)
24 The Assessment of Follow-On Biologic Products
487(20)
24.1 Introduction
487(2)
24.2 Regulatory Requirements
489(1)
24.3 Criteria for Biosimilarity
490(5)
24.3.1 Absolute Change versus Relative Change
490(1)
24.3.2 Aggregated versus Disaggregated
491(1)
24.3.3 Moment-Based Criteria versus Probability-Based Criteria
492(1)
24.3.4 Similarity Factor for Dissolution Profile Comparison
493(1)
24.3.5 Consistency in Manufacturing Process/Quality Control
494(1)
24.4 Scientific Issues
495(9)
24.4.1 Biosimilarity in Biological Activity
495(1)
24.4.2 Similarity in Size and Structure
495(1)
24.4.3 The Problem of Immunogenicity
495(1)
24.4.4 Manufacturing Process
496(1)
24.4.5 Statistical Considerations
497(7)
24.5 Assessing Similarity Using Genomic Data
504(1)
24.6 Concluding Remarks
505(2)
25 Generalizability/Reproducibility Probability
507(20)
25.1 Introduction
507(2)
25.2 The Estimated Power Approach
509(5)
25.2.1 Two Samples with Equal Variances
509(2)
25.2.2 Two Samples with Unequal Variances
511(2)
25.2.3 Parallel-Group Designs
513(1)
25.3 The Confidence Bound Approach
514(2)
25.4 The Bayesian Approach
516(4)
25.5 Applications
520(5)
25.5.1 Substantial Evidence with a Single Trial
520(1)
25.5.2 Sample Size Adjustments
521(1)
25.5.3 Generalizability between Patient Populations
522(3)
25.6 Concluding Remarks
525(2)
26 Good Review Practices
527(16)
26.1 Introduction
527(1)
26.2 Regulatory Process and Requirements
528(4)
26.2.1 Investigational New Drug Application
529(1)
26.2.2 New Drug Application
530(2)
26.3 Good Review Practices
532(2)
26.3.1 Fundamental Values
532(1)
26.3.2 Implementation of GRP
533(1)
26.3.3 Remarks
533(1)
26.4 Obstacles and Challenges
534(7)
26.4.1 No Gold Standards for Evaluation of Clinical Data
534(2)
26.4.2 One-Fits-All Criterion for Bioequivalence Trials
536(1)
26.4.3 Bayesian Statistics in Drug Evaluation
537(1)
26.4.4 Adaptive Design Methods in Clinical Trials
537(4)
26.5 Concluding Remarks
541(2)
27 Probability of Success
543(10)
27.1 Introduction
543(1)
27.2 Go/No-Go Decision in Development Process
544(5)
27.2.1 Simple Approach for Decision Making
544(1)
27.2.2 Decision-Tree Approach
545(2)
27.2.3 An Example
547(2)
27.3 POS Assessment
549(1)
27.4 Concluding Remarks
550(3)
References 553(24)
Index 577
Shein-Chung Chow, Ph.D., is a professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine. Dr. Chow is also an adjunct professor of clinical sciences at DukeNational University of Singapore Graduate Medical School and the editor-in-chief of the Journal of Biopharmaceutical Statistics. He has authored or co-authored more than 200 papers and 19 books, including the recently published Handbook of Adaptive Designs in Pharmaceutical and Clinical Development. He earned his Ph.D. in statistics from the University of WisconsinMadison.
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