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E-raamat: Handbook of Statistical Methods for Randomized Controlled Trials

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Statistical concepts provide scientific framework in experimental studies, including randomized controlled trials. In order to design, monitor, analyze and draw conclusions scientifically from such clinical trials, clinical investigators and statisticians should have a firm grasp of the requisite statistical concepts. The Handbook of Statistical Methods for Randomized Controlled Trials presents these statistical concepts in a logical sequence from beginning to end and can be used as a textbook in a course or as a reference on statistical methods for randomized controlled trials.

Part I provides a brief historical background on modern randomized controlled trials and introduces statistical concepts central to planning, monitoring and analysis of randomized controlled trials. Part II describes statistical methods for analysis of different types of outcomes and the associated statistical distributions used in testing the statistical hypotheses regarding the clinical questions. Part III describes some of the most used experimental designs for randomized controlled trials including the sample size estimation necessary in planning. Part IV describe statistical methods used in interim analysis for monitoring of efficacy and safety data. Part V describe important issues in statistical analyses such as multiple testing, subgroup analysis, competing risks and joint models for longitudinal markers and clinical outcomes. Part VI addresses selected miscellaneous topics in design and analysis including multiple assignment randomization trials, analysis of safety outcomes, non-inferiority trials, incorporating historical data, and validation of surrogate outcomes.

Arvustused

"This book is the product of a large and outstanding group of editors and collaborative authors who undertook a huge effort of summarizing, in one volume, a subject spanning a wide crosssection of topics related to clinical trials. ... Throughout, many topics are illustrated with examples of recently reported trials adding to the applicability of the corresponding theory. The emphasis on sample size estimation is a very nice touch and a strong feature of the book. In some cases, authors have included code in R and SAS to assist users." -Daniel Zelterman in Technometrics, July 2022

"This book has the power to be a reference for teaching or research. This book provides a comprehensive guide to statistical methods relevant to randomized, controlled clinical trials. The authors explain each chapter in detail, providing an in-depth understanding of the concepts and techniques involved. The authors in this book are leading experts in their fields. The authors have extensive experience and knowledge in statistics and randomized controlled clinical trials, thereby lending authority and reliability to the content presented. A good balance between the statistical theory underlying the method and practical application in the context of randomized controlled clinical trials. Readers can understand the theoretical basics and observe the practical application of the methods to real data. This book includes examples and case studies that use real data. Readers gain a practical understanding of the methods taught in the book and an overview of their application in randomized controlled clinical trials." -Tri Astari in Technometrics, February 2024

Preface xix
List of Figures
xxi
List of Tables
xxv
Contributors xxxi
I Introduction to Randomized Controlled Trials
1(8)
1 Introduction
3(6)
Kyung Mann Kim
1.1 Historical Background
3(1)
1.2 Statistical Concepts
4(1)
1.3 Organization of the Handbook
5(4)
Bibliography
6(3)
II Analytic Methods for Randomized Controlled Trials
9(128)
2 Binary and Ordinal Outcomes
11(34)
Garrett M. Fitzmaurice
Stuart R. Lipsitz
2.1 Introduction
11(2)
2.2 Analysis of 2 × 2 Contingency Tables
13(4)
2.3 Analysis of R x C Contingency Tables
17(3)
2.4 Analysis of Stratified 2 × 2 Contingency Tables
20(2)
2.5 Regression Models for Binary Outcomes
22(6)
2.5.1 Logistic regression
24(2)
2.5.2 Estimation and inference for logistic regression
26(1)
2.5.3 Exact logistic regression
27(1)
2.5.4 Example
28(1)
2.6 Regression Models for Ordinal Outcomes
28(9)
2.6.1 Proportional odds model
29(4)
2.6.2 Some alternative models for ordinal outcomes
33(1)
2.6.3 Example
34(3)
2.7 Adjustment for Baseline Response
37(4)
2.8 Concluding Remarks
41(4)
Bibliography
42(3)
3 Continuous Outcomes
45(18)
Fang-Shu Ou
3.1 Introduction
45(1)
3.2 The t-Test (One Population)
46(1)
3.3 The t-Test (Two Populations)
47(2)
3.4 Mann-Whitney U-Test
49(1)
3.5 Paired Tests
50(2)
3.5.1 Paired t-test
51(1)
3.5.2 Wilcoxon signed rank test
51(1)
3.6 Multiple Comparisons
52(1)
3.7 Regression
53(7)
3.7.1 Residuals
54(2)
3.7.2 Inference for linear regression
56(2)
3.7.3 ANCOVA models
58(1)
3.7.4 Nonlinear regression
59(1)
3.8 Conclusion
60(3)
Bibliography
60(3)
4 Time to Event Data
63(18)
Daniel Scharfstein
Yuxin Zhu
Anastasios Tsiatis
4.1 Introduction
63(1)
4.2 ACTG 320
64(1)
4.3 Mathematical Fundamentals
64(3)
4.3.1 Notation
64(2)
4.3.2 Hazard
66(1)
4.3.3 Censoring and observed data
66(1)
4.4 Estimation of Survival Distribution
67(1)
4.5 Hypothesis Testing
68(2)
4.6 Cox Regression Model
70(3)
4.7 Informative Censoring
73(2)
4.8 Conclusion
75(6)
Bibliography
77(4)
5 Count Data
81(16)
Xin He
Jianguo "Tony" Sun
5.1 Introduction
81(1)
5.2 Regression Analysis of Simple Count Data
82(4)
5.2.1 Poisson regression for count
83(1)
5.2.2 Negative binomial regression for count
84(1)
5.2.3 Poisson and negative binomial regression for rate
85(1)
5.2.4 Other models for simple count data
86(1)
5.3 Regression Analysis of Correlated Count Data: Likelihood-Based Approaches
86(3)
5.3.1 Maximum pseudo-likelihood estimation for the Poisson model
87(1)
5.3.2 Maximum likelihood estimation for the Poisson model
88(1)
5.3.3 Maximum likelihood estimation for the negative binomial model
88(1)
5.4 Regression Analysis of Correlated Count Data: Distribution-Free Approaches
89(3)
5.4.1 Conditional estimating equation method
89(1)
5.4.2 Unconditional estimating equation method
90(2)
5.4.3 Analysis of the National Cooperative Gallstone Study
92(1)
5.5 Discussion and Concluding Remarks
92(5)
Bibliography
93(4)
6 Longitudinal Data
97(20)
Soeun Kim
Myunghee Cho Paik
6.1 Introduction
97(1)
6.2 Generalized Linear Models
98(1)
6.3 Generalized Estimating Equations
98(4)
6.3.1 Notations
99(1)
6.3.2 Asymptotic properties
99(1)
6.3.3 Efficiency
100(1)
6.3.4 Model selection criterion in GEE
101(1)
6.4 Generalized Linear Mixed Models
102(2)
6.4.1 Notations
102(1)
6.4.2 Population average versus subject-specific model
102(1)
6.4.3 Estimation procedures
103(1)
6.4.3.1 Marginal likelihood
103(1)
6.4.3.2 Conditional likelihood
104(1)
6.5 Test Statistics Under Randomization
104(3)
6.5.1 Notations
105(1)
6.5.2 Score-type test for GEE under randomization
105(1)
6.5.3 Score test for GLMMs under randomization
106(1)
6.6 Handling Missing Data in Clinical Trials
107(2)
6.6.1 Missing data in GEE
108(1)
6.6.2 Missing data in GLMMs
108(1)
6.7 Case Study
109(8)
Bibliography
113(4)
7 Recurrent Events
117(20)
Yujie Zhong
Richard Cook
7.1 Introduction
117(2)
7.1.1 Recurrent event data
117(1)
7.1.2 Data from a cystic fibrosis Trial
118(1)
7.2 Notation and Model Formulation
119(7)
7.2.1 Analysis considerations with recurrent event data
119(1)
7.2.2 Methods based on rate and mean functions
120(3)
7.2.3 Censoring, Likelihood, and Marginal Methods
123(1)
7.2.4 Assessment based on exacerbations in cystic fibrosis
124(2)
7.3 Sample Size Based on Proportional Rate Functions
126(2)
7.3.1 Derivations under a negative binomial model
126(2)
7.3.2 Illustrative sample size calculation
128(1)
7.4 Other Considerations in Recurrent Event Analyses
128(4)
7.4.1 Issues regarding causal inference
128(1)
7.4.2 Marginal multivariate failure times models
129(1)
7.4.3 Adaptive two-stage sample size estimation
130(1)
7.4.4 Recurrent and terminal events
131(1)
7.5 Discussion
132(5)
Acknowledgments
132(1)
Bibliography
133(4)
III Design of Randomized Controlled Trials
137(178)
8 Cross-Over Designs
139(24)
Stephen Senn
8.1 Introduction
140(1)
8.2 Some Examples
140(5)
8.2.1 Example 1: An AB/BA design
141(1)
8.2.2 Example 2: A design in three treatments, three periods, and six sequences
141(1)
8.2.3 Example 3: An incomplete blocks design with fewer periods than treatments
142(2)
8.2.4 Example 4: A replicate cross-over design with more periods than treatments
144(1)
8.2.5 Example 5: A replicate bioequivalence study comparing two formulations in four periods
145(1)
8.3 General Considerations
145(1)
8.3.1 Phase of drug development
145(1)
8.3.2 Suitable indications
145(1)
8.4 Issues in Analysis
146(4)
8.4.1 Models for cross-over trials
146(1)
8.4.2 Patient effects and variance structures
147(1)
8.4.3 Carry-over effects
148(1)
8.4.4 Residual degrees of freedom and error estimation
148(2)
8.5 Examples of Analysis
150(5)
8.5.1 Basic estimator approach
150(2)
8.5.2 Two-sample t-test approach
152(1)
8.5.3 Linear and mixed models
152(1)
8.5.4 Testing for carry-over
153(1)
8.5.5 8.5.5 An unbiased estimate of the treatment effect
154(1)
8.5.6 The two-stage procedure
154(1)
8.6 Issues in Design
155(3)
8.6.1 Choosing sequences
155(1)
8.6.2 Other issues
156(1)
8.6.3 Planning the sample size
157(1)
8.7 N-of-1 trials
158(1)
8.8 Conclusion
159(1)
8.9 Further reading
159(1)
8.10 Acknowledgement
159(4)
Bibliography
159(4)
9 Factorial Designs
163(28)
Bibhas Chakraborty
Palash Ghosh
9.1 Introduction
163(1)
9.2 Different Usages of Factorial Designs
164(6)
9.2.1 Efficiency of confirmatory trials: Evaluation of more than one Intervention in a single study -
165(1)
9.2.2 Screening trials: Developing multicomponent interventions
166(3)
9.2.3 Situations where factorial designs are not suitable
169(1)
9.3 Full Factorial Designs: A Theoretical Background
170(4)
9.4 Fractional Factorial Designs
174(2)
9.5 Analysis Strategies
176(3)
9.6 Follow-up Studies: Developing Multicomponent Interventions
179(1)
9.7 Power and Sample Size Considerations
180(4)
9.8 Discussion
184(7)
Bibliography
185(6)
10 Cluster Randomized Designs
191(24)
Martin Bland
Mona Kanaan
10.1 What is a Cluster Randomized Trial?
191(1)
10.2 The Problem of Clustering
192(2)
10.3 Summary Statistics
194(2)
10.4 The Intra-Cluster Correlation Coefficient and the Design Effect
196(2)
10.5 Baseline and Other Adjustments
198(1)
10.6 Robust Standard Errors
199(2)
10.7 Multilevel Modeling
201(2)
10.8 Generalized Estimating Equations (GEE) Models
203(1)
10.9 Stepped Wedge Designs
204(2)
10.10 Sample Size Estimation
206(2)
10.11 Practical Problems of Cluster Randomized Trials
208(7)
Bibliography
210(5)
11 Randomization, Stratification, and Outcome-Adaptive Allocation
215(28)
Oleksandr Sverdlov
Yevgen Ryeznik
11.1 Introduction
215(3)
11.2 Simple and Restricted Randomization
218(7)
11.3 Stratified and Covariate-Adaptive Randomization
225(8)
11.4 Outcome-Adaptive Randomization
233(3)
11.5 Concluding Remarks
236(7)
Bibliography
238(5)
12 Background to Sample Size Calculations
243(32)
Jo Rothwell
Cindy Cooper
Steven Julious
Mike Campbell
12.1 Introduction
244(1)
12.2 Types of Trials
244(2)
12.2.1 Parallel group trials
244(1)
12.2.2 Cross-over trials
245(1)
12.3 Continuous Outcomes
246(16)
12.3.1 Superiority trials
246(1)
12.3.1 Parallel group trials
247(1)
12.3.1 Quick results
248(1)
12.3.1 Worked example 1
249(1)
12.3.1 Cross-over trials
249(1)
12.3.1 Quick results
250(1)
12.3.1 Worked example 2
251(1)
12.3.2 Equivalence trials
251(2)
12.3.2 Parallel group trials
253(2)
12.3.2 Worked example 3
255(1)
12.3.2 Cross-over trials
255(2)
12.3.2 Worked example 4
257(2)
12.3.3 Non-inferiority trials
259(1)
12.3.3 Parallel group trials
259(1)
12.3.3 Worked example 5
259(1)
12.3.3 Cross-over trials
260(1)
12.3.3 Worked example 6
260(2)
12.4 Binary Outcomes
262(10)
12.4.1 Superiority trials
262(1)
12.4.1 Parallel group trials
262(2)
12.4.1 Method 2
264(1)
12.4.1 Worked example 7
264(2)
12.4.1 Cross-over trials
266(1)
12.4.1 Worked example 8
267(2)
12.4.2 Equivalence trials
269(1)
12.4.2 Parallel group trials
269(1)
12.4.2 Worked example 9
269(1)
12.4.2 Cross-over trials
270(1)
12.4.3 Non-inferiority trials
270(1)
12.4.3 Parallel group trials
270(1)
12.4.3 Worked example 10
271(1)
12.5 Final Remarks
272(3)
Bibliography
272(3)
13 Sample Size Estimation and Power Analysis: Time to Event Data
275(26)
Oliver Bautista
Keaven Anderson
13.1 Introduction
275(1)
13.2 Methods for Sample Size Estimation and Power Analysis
276(3)
13.2.1 Approaches relating to acquisition of events
276(1)
13.2.2 Estimation of required number of events: no accounting of other design parameters
277(1)
13.2.3 Estimation of required number of events: with accounting of other design parameters
278(1)
13.3 Case Studies
279(14)
13.3.1 Rare events with non-proportional hazard ratio
279(1)
13.3.1 The study as designed
279(1)
13.3.1 The study as it unfolded
279(1)
13.3.1 Insights gleaned from the study
280(1)
13.3.1 Alternative strategies
281(1)
13.3.1 Alternative strategy example
282(2)
13.3.2 An oncology study
284(5)
13.3.3 A diabetes noninferiority study
289(4)
13.4 Special Topics and Recent Developments
293(8)
13.4.1 Treatment effects beyond hazard ratios
293(1)
13.4.2 Sample size re-estimation
294(3)
Bibliography
297(4)
14 Sample Size Estimation and Power Analysis: Longitudinal Data
301(14)
Sin-Ho Jung
14.1 Introduction
301(1)
14.2 Generalized Estimating Equations (GEE) Method
302(3)
14.2.1 Continuous outcome variable case
303(1)
14.2.2 Binary outcome variable case
304(1)
14.3 Power Analysis and Sample Size Estimation
305(2)
14.3.1 Continuous outcome variable case
306(1)
14.3.2 Binary outcome variable case
306(1)
14.4 Modelling Missing Pattern and Correlation Structure
307(1)
14.4.1 Missing pattern
308(1)
14.4.2 Correlation structure
308(1)
14.5 Examples
308(2)
14.5.1 Labor pain study (Continuous outcome case)
309(1)
14.5.2 Design of an RCT based on GENISOS (binary outcome case)
309(1)
14.6 Discussions
310(5)
Bibliography
311(4)
IV Monitoring of Randomized Controlled Trials
315(80)
15 Group Sequential Methods
317(22)
Michael Proschan
15.1 Group Sequential Methods
317(12)
15.1.1 A unified framework
318(6)
15.1.2 Boundaries
324(5)
15.2 The Effect of Monitoring on Power
329(1)
15.3 Futility/Stochastic Curtailment
330(4)
15.4 Problems with Post-Trial Inference
334(1)
15.5 Conclusions
335(4)
Bibliography
336(3)
16 Sample Size Re-Estimation
339(32)
Tobias Mutze
Tim Friede
16.1 Introduction
339(3)
16.2 Nuisance Parameter Based Sample Size Re-Estimation
342(14)
16.2.1 Sample size re-estimation for normal data
342(1)
16.2.1 Motivating example
342(1)
16.2.1 Statistical model and sample size re-estimation
342(1)
16.2.1 Unblinded nuisance parameter estimation
343(1)
16.2.1 Blinded nuisance parameter estimation
344(1)
16.2.1 Comparison of sample size re-estimation procedures
345(4)
16.2.2 Sample size re-estimation for count data
349(1)
16.2.2 Motivating example
350(1)
16.2.2 Negative binomial outcomes
351(1)
16.2.3 Further issues and recent developments
352(1)
16.2.3 Non-inferiority trials
352(1)
16.2.3 Controlling the type I error rate
353(1)
16.2.3 Size of the internal pilot study
353(1)
16.2.3 Covariates
354(1)
16.2.3 Other endpoints and more complex designs
355(1)
16.2.3 Multi-arm trials
355(1)
16.2.3 Incorporating historical data into the sample size re-estimation
356(1)
16.3 Effect-Based Sample Size Re-Estimation
356(6)
16.3.1 Controlling the type I error rate
357(4)
16.3.2 Sample size adaptation
361(1)
16.3.3 Further issues and recent developments
361(1)
16.4 Discussion
362(9)
Acknowledgments
363(1)
Bibliography
363(8)
17 Adaptive Designs
371(24)
Gemot Wassmer
Franz Koenig
Martin Posch
17.1 Introduction
371(1)
17.2 General Principles
372(6)
17.2.1 The combination testing principle
373(2)
17.2.2 The closed testing principle
375(1)
17.2.3 Adaptive designs for multiple hypotheses
375(2)
17.2.4 Assessing the performance of an adaptive design
377(1)
17.3 Treatment Arm Selection Designs
378(4)
17.3.1 The procedure
378(2)
17.3.2 Binary and survival endpoints
380(2)
17.3.3 Case studies
382(1)
17.4 Population Enrichment Designs
382(4)
17.4.1 The procedure
383(2)
17.4.2 Effect specification
385(1)
17.4.3 Binary and survival endpoints
385(1)
17.4.4 Case studies
386(1)
17.5 Discussion and Further Developments
386(9)
Acknowledgments
387(1)
Bibliography
388(7)
V Practical Issues in Analysis of Randomized Controlled Trials
395(90)
18 Multiple Testing
397(26)
Yi Liu
Jason Hsu
Szu-Yu Tang
18.1 Error Rates in Multiple Comparisons
398(1)
18.2 Principles of Multiple Testing
399(2)
18.2.1 Partitioning principle
400(1)
18.2.2 Closed testing principle
401(1)
18.3 A Simple Example
401(2)
18.4 Shortcutting
403(4)
18.4.1 Holm's method is a shortcut
405(1)
18.4.2 Hochberg's method is also a shortcut
405(2)
18.5 Paths in Decision-Making
407(4)
18.5.1 Decision path respecting principle
408(1)
18.5.2 A specific dose x endpoint example
409(2)
18.6 Setting Priorities in Multiple Testing for Each Study
411(4)
18.6.1 The graphical approach
413(2)
18.7 Logical Relationships Among Parameters Tested
415(4)
18.7.1 Logic induced in multiple test construction
416(2)
18.7.2 Logic inherent in scientific parameters
418(1)
18.8 Going Forward
419(4)
Bibliography
419(4)
19 Subgroup Analysis
423(20)
Rui Wang
19.1 Introduction
423(1)
19.2 Methods for Conducting Subgroup Analyses
424(13)
19.2.1 Commonly used methods
424(4)
19.2.2 Qualitative interaction
428(3)
19.2.3 Graphical methods
431(3)
19.2.4 Multivariate tests of interaction
434(3)
19.3 Power Consideration of Subgroup Analysis
437(1)
19.4 Subgroup Analysis Reporting and Interpretation
437(1)
19.5 Final Remarks
437(6)
Bibliography
438(5)
20 Competing Risks
443(20)
Haesook Kim
20.1 Introduction
443(1)
20.2 Cumulative Incidence Function in the Presence of Competing Risks
444(4)
20.2.1 Cumulative incidence function
445(1)
20.2.2 Estimation of CIF in the presence of competing risks
445(3)
20.3 Testing for Differences between Cumulative Incidence Curves in the Presence of Competing Risks
448(3)
20.3.1 Gray test
448(1)
20.3.2 Estimation of Gray statistic
449(2)
20.4 Competing Risks Regression Analysis
451(5)
20.4.1 Cause-specific hazard regression model
452(1)
20.4.2 Fine and Gray model
452(1)
20.4.3 Klein and Andersen model
453(3)
20.4.4 Remarks
456(1)
20.5 Conclusion
456(1)
20.6 Computing Tools
457(6)
Acknowledgments
458(1)
Bibliography
458(5)
21 Joint Models for Longitudinal and Time to Event Data
463(22)
Helene Jacqmin-Gadda
Loic Ferrer
Cecile Proust-Lima
21.1 Introduction
463(2)
21.2 Illustrative Example
465(2)
21.3 Joint Shared Random-Effect Models
467(8)
21.3.1 Model definition for Gaussian markers
467(2)
21.3.2 Model definition for discrete markers
469(1)
21.3.3 Estimation
469(1)
21.3.3 Likelihood
469(1)
21.3.3 Bayesian estimation
470(1)
21.3.3 Model diagnostic
471(1)
21.3.4 Joint shared random-effect models for clinical trials
471(1)
21.3.4 Distinguishing direct and indirect treatment effects
471(2)
21.3.4 Incomplete data
473(2)
21.4 Joint Latent Class Models
475(4)
21.4.1 Model definition
475(1)
21.4.2 Estimation
476(1)
21.4.2 Likelihood
476(1)
21.4.2 Model diagnostic
476(1)
21.4.3 Joint latent class models for clinical trials
477(2)
21.5 Conclusion and Recent Developments
479(6)
Acknowledgments
480(1)
Bibliography
480(5)
VI Miscellaneous Topics in Randomized Controlled Trials
485(115)
22 Design and Analysis Methods for Developing Personalized Treatment Rules
487(22)
Emily Butler
Michael Kosorok
22.1 Introduction
487(1)
22.2 Study Design
488(4)
22.3 Analysis Techniques: Single Stage
492(4)
22.4 Analysis Techniques: Multiple Stages
496(5)
22.5 Related Topics
501(4)
22.5.1 Variable selection
501(2)
22.5.2 Multiple outcomes
503(1)
22.5.3 DTRs for observational data
504(1)
22.6 Conclusion
505(4)
Bibliography
506(3)
23 Safety Evaluation in Clinical Trials
509(18)
H. Amy Xia
Brenda J. Crowe
Jesse A. Berlin
23.1 Introduction
509(2)
23.2 Elements of a Systematic Approach to Clinical Trial Safety Data Evaluation
511(1)
23.2.1 The program safety analysis plan (PSAP)
511(1)
23.2.2 Facilitating combining data across studies, including planning metaanalyses (be prepared)
512(1)
23.3 Approaches to Characterizing the Product Safety Profile
512(3)
23.3.1 Known or pre-specified safety issues
513(1)
23.3.1 Specific safety issues that should always be considered for all products
513(1)
23.3.1 Product-specific adverse events of special interest (AESIs)
513(1)
23.3.1 Adverse events not specified in advance
513(1)
23.3.2 Data sources for safety evaluation including specific safety studies
514(1)
23.4 Planning for Clinical Data Collection and Standardization
515(2)
23.4.1 Definition of safety outcomes and adjudication
515(1)
23.4.2 Standardization of safety data collection
516(1)
23.5 Safety Data Analysis and Reporting
517(5)
23.5.1 Considerations for individual studies
518(1)
23.5.1 Defining the safety analysis set
518(1)
23.5.1 Accounting for time on or off treatment
518(1)
23.5.2 Meta-analysis of adverse event data
519(1)
23.5.3 Multiplicity
520(1)
23.5.4 Signal detection for common events
521(1)
23.5.5 Descriptive analysis of infrequent adverse events
521(1)
23.5.6 Reporting
522(1)
23.6 Conclusions
522(5)
Bibliography
523(4)
24 Non-Inferiority Trials
527(18)
Brian L. Wiens
24.1 Background and History
527(1)
24.2 Basics
528(9)
24.2.1 Historical studies
528(1)
24.2.2 Parameters and margins
529(3)
24.2.3 Study design and conduct
532(1)
24.2.4 Test statistics, confidence intervals and decision rules
533(2)
24.2.5 Reporting and interpretation
535(1)
24.2.6 Power and sample size assessment
535(1)
24.2.7 Equivalence and non-inferiority
536(1)
24.3 Issues and Evolving Ideas
537(6)
24.3.1 Analysis sets
537(2)
24.3.2 Missing data
539(2)
24.3.3 Adaptive designs
541(2)
24.4 Conclusions
543(2)
Bibliography
543(2)
25 Incorporating Historical Data into Randomized Controlled Trials
545(22)
Heinz Schmidli
Sandro Gsteiger
Beat Neuenschwander
25.1 Introduction
546(1)
25.2 Case Study
546(1)
25.3 Meta-Analytic-Predictive Approach
547(5)
25.3.1 Hierarchical model
547(1)
25.3.2 Mixture approximation for priors
548(1)
25.3.3 Robustness to a prior-data conflict
549(1)
25.3.4 Prior effective sample size
550(1)
25.3.5 Operating characteristics
551(1)
25.3.6 Analysis
551(1)
25.4 Other Approaches
552(3)
25.4.1 Meta-analytic-combined approach
552(1)
25.4.2 Bias models
553(1)
25.4.3 Commensurate priors
553(1)
25.4.4 Power priors
553(1)
25.4.5 Test-then-pool
554(1)
25.4.6 How much borrowing?
555(1)
25.5 Extensions
555(5)
25.5.1 Individual patient data and aggregate data
555(3)
25.5.2 Non-inferiority trials
558(2)
25.6 Discussion
560(1)
25.7 Appendix
561(6)
25.7.1 WinBUGS code
561(1)
25.7.2 SAS code
561(1)
Bibliography
562(5)
26 Evaluation of Surrogate Endpoints
567(33)
Geert Molenberghs
Ziv Shkedy
Tomasz Burzykowski
Marc Buyse
Ariel Alonso Abad
Wim Van der Elst
26.1 Introduction
568(1)
26.2 Data from a Single Trial
569(2)
26.2.1 Definition and criteria
570(1)
26.2.2 The proportion explained
571(1)
26.2.3 The relative effect
571(1)
26.3 A Meta-analytic Framework for Normally Distributed Outcomes
571(2)
26.3.1 A meta-analytic approach
571(2)
26.4 Non-Gaussian Endpoints
573(4)
26.4.1 Two binary endpoints
574(1)
26.4.2 Two failure-time endpoints
575(1)
26.4.3 An ordinal surrogate and a survival endpoint
575(1)
26.4.4 Binary and normally distributed endpoints
575(1)
26.4.5 Longitudinal endpoints
576(1)
26.5 Alternatives and Extensions
577(1)
26.6 Prediction and Design Aspects
578(2)
26.7 Case Studies
580(12)
26.7.1 A meta-analysis of five clinical trials in schizophrenia
580(1)
26.7.1 Analysis of continuous endpoints
581(1)
26.7.1 Analysis of the categorical endpoints
582(2)
26.7.2 Prostate-specific antigen (PSA)
584(1)
26.7.2 PSA as a surrogate in multiple trials
585(1)
26.7.3 Surrogate endpoints in gastric cancer
586(1)
26.7.3 Resectable gastric cancer: can DFS be used a surrogate for OS?
586(2)
26.7.3 Advanced gastric cancer: can PFS be used as a surrogate for OS?
588(1)
26.7.3 Contrasting conclusions about DFS and PFS
589(3)
26.8 Concluding Remarks
592(8)
Acknowledgments
592(1)
Bibliography
593(7)
Index 600
KyungMann Kim is Professor of Biostatistics and Statistics and Director of Clinical Trials Program, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison. He is a former associate editor of Biometrics and an elected Fellow of the American Statistical Association, the Society for Clinical Trials, and the American Association for Advancement of Science.

Frank Bretz is a Distinguished Quantitative Research Scientist at Novartis. He is also an Adjunct Professor at the Hannover Medical School (Germany) and the Medical University Vienna (Austria). He is a former editor-in-chief of Statistics in Biopharmaceutical Research. a Fellow of the American Statistical Association, and a recipient of the Susanne-Dahms-Medal from the German Region of the International Biometric Society.

Ying Kuen (Ken) Cheung is Professor of Biostatistics and Associate Dean for Faculty in the Mailman School of Public Health at Columbia University. He is a recipient of the IBM Faculty Award on Big Data and Analytics. He is a Fellow of the American Statistical Association and a Fellow of the New York Academy of Medicine.

Lisa Hampson is a Director in Statistical Methodology at Novartis.
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