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by N. Balakrishnan
Methods and Applications of Statistics in Clinical Trials, Volume 2: Planning, Analysis, and Inferential Methods
Cover
Half Title page
Title page
Copyright page
Contributors
Preface
Chapter 1: Analysis of Over- and Underdispersed Data
1.1 Introduction
1.2 Overdispersed Binomial and Count Models
1.3 Other Approaches to Account for Overdispersion
1.4 Underdispersion
1.5 Software Notes
References
Chapter 2: Analysis of Variance (ANOVA)
2.1 Introduction
2.2 Factors, Levels, Effects, and Cells
2.3 Cell Means Model
2.4 One-Way Classification
2.5 Parameter Estimation
2.6 The R(.) Notation—Partitioning Sum of Squares
2.7 ANOVA—Hypothesis of Equal Means
2.8 Multiple Comparisons
2.9 Two-Way Crossed Classification
2.10 Balanced and Unbalanced Data
2.11 Interaction Between Rows and Columns
2.12 Analysis of Variance Table
References
Chapter 3: Assessment of Health-Related Quality of Life
3.1 Introduction
3.2 Choice of HRQOL Instruments
3.3 Establishment of Clear Objectives in HRQOL Assessments
3.4 Methods for HRQOL Assessment
3.5 HRQOL as the Primary End Point
3.6 Interpretation of HRQOL Results
3.7 Examples
3.8 Conclusion
References
Further Reading
Chapter 4: Bandit Processes and Response-Adaptive Clinical Trials: The Art of Exploration Versus Exploitation
4.1 Introduction
4.2 Exploration Versus Exploitation with Complete Observations
4.3 Exploration Versus Exploitation with Censored Observations
4.4 Conclusion
References
Chapter 5: Bayesian Dose-Finding Designs in Healthy Volunteers
5.1 Introduction
5.2 A Bayesian Decision-Theoretic Design
5.3 An Example of Dose Escalation in Healthy Volunteer Studies
5.4 Discussion
References
Chapter 6: Bootstrap
6.1 Introduction
6.2 Plug-In Principle
6.3 Monte Carlo Sampling— The “Second Bootstrap Principle”
6.4 Bias and Standard Error
6.5 Examples
6.6 Model Stability
6.7 Accuracy of Bootstrap Distributions
6.8 Bootstrap Confidence Intervals
6.9 Hypothesis Testing
6.10 Planning Clinical Trials
6.11 How Many Bootstrap Samples Are Needed
6.12 Additional References
References
Chapter 7: Conditional Power in Clinical Trial Monitoring
7.1 Introduction
7.2 Conditional Power
7.3 Weight-Averaged Conditional Power or Bayesian Predictive Power
7.4 Conditional Power of a Different Kind: Discordance Probability
7.5 Analysis of a Randomized Trial
7.6 Conditional Power: Pros and Cons
References
Chapter 8: Cost-Effectiveness Analysis
8.1 Introduction
8.2 Definitions and Design Issues
8.3 Cost and Effectiveness Data
8.4 The Analysis of Costs and Outcomes
8.5 Robustness and Generalizability in Cost-Effectiveness Analysis
References
Further Reading
Chapter 9: Cox-Type Proportional Hazards Models
9.1 Introduction
9.2 Cox Model for Univariate Failure Time Data Analysis
9.3 Marginal Models for Multivariate Failure Time Data Analysis
9.4 Practical Issues in Using the Cox Model
9.5 Examples
9.6 Extensions
9.7 Softwares and Codes
References
Further Reading
Chapter 10: Empirical Likelihood Methods in Clinical Experiments
10.1 Introduction
10.2 Classical EL: Several Ingredients for Theoretical Evaluations
10.3 The Relationship Between Empirical Likelihood and Bootstrap Methodologies
10.4 Bayes Methods Based on Empirical Likelihoods
10.5 Mixtures of Likelihoods
10.6 An Example: ROC Curve Analyses Based on Empirical Likelihoods
10.7 Applications of Empirical Likelihood Methodology in Clinical Trials or Other Data Analyses
10.8 Concluding Remarks
Appendix
References
Chapter 11: Frailty Models
11.1 Introduction
11.2 Univariate Frailty Models
11.3 Multivariate Frailty Models
11.4 Software
References
Chapter 12: Futility Analysis
12.1 Introduction
12.2 Common Statistical Approaches to Futility Monitoring
12.3 Examples
12.4 Discussion
References
Further Reading
Chapter 13: Imaging Science in Medicine I: Overview
13.1 Introduction
13.2 Advances in Medical Imaging
13.3 Evolutionary Developments in Imaging
13.4 Conclusion
References
Chapter 14: Imaging Science in Medicine, II: Basics of X-Ray Imaging
14.1 Introduction to Medical Imaging: Different Ways of Creating Visible Contrast Among Tissues
14.2 What the Body Does to the X-Ray Beam: Subject Contrast From Differential Attenuation of the X-Ray Beam by Various Tissues
14.3 What the X-Ray Beam Does to the Body: Known Medical Benefits Versus Possible Radiogenic Risks
14.4 Capturing the Visual Image: Analog (20th Century) X-Ray Image Receptors
Chapter 15: Imaging Science in Medicine, III: Digital (21st Century) X-Ray Imaging
15.1 The Computer in Medical Imaging
15.2 The Digital Planar X-Ray Modalities: Computed Radiography and Digital Radiography and Fluoroscopy
15.3 Digital Fluoroscopy and Digital Subtraction Angiography
15.4 Digital Tomosynthesis: Planar Imaging in Three Dimensions
15.5 Computed Tomography: Superior Contrast in Three-Dimensional X-Ray Attenuation Maps
Chapter 16: Intention-to-Treat Analysis
16.1 Introduction
16.2 Missing Information
16.3 The Intention-to-Treat Design
16.4 Efficiency of the Intent-to-Treat Analysis
16.5 Compliance-Adjusted Analyses
16.6 Conclusion
References
Further Reading
Chapter 17: Interim Analyses
17.1 Introduction
17.2 Opportunities and Dangers of Interim Analyses
17.3 The Development of Techniques for Conducting Interim Analyses
17.4 Methodology for Interim Analyses
17.5 An Example: Statistics for Lamivudine
17.6 Interim Analyses in Practice
17.7 Conclusions
References
Chapter 18: Interrater Reliability
18.1 Definition
18.2 The Importance of Reliability in Clinical Trials
18.3 How Large a Reliability Coefficient Is Large Enough?
18.4 Design and Analysis of Reliability Studies
18.5 Estimate of the Reliability Coefficient—Parametric
18.6 Estimation of the Reliability Coefficient— Nonparametric
18.7 Estimation of the Reliability Coefficient—Binary
18.8 Estimation of the Reliability Coefficient—Categorical
18.9 Strategies to Increase Reliability (Spearman–Brown Projection)
18.10 Other Types of Reliabilities
References
Chapter 19: Intrarater Reliability
19.1 Introduction
19.2 Intrarater Reliability for Continuous Scores
19.3 Nominal Scale Score Data
19.4 Ordinal and Interval Score Data
19.5 Concluding Remarks
References
Further Reading
Chapter 20: Kaplan—Meier Plot
20.1 Introduction
20.2 Estimation of Survival Function
20.3 Additional Topics
References
Chapter 21: Logistic Regression
21.1 Introduction
21.2 Fitting the Logistic Regression Model
21.3 The Multiple Logistic Regression Model
21.4 Fitting the Multiple Logistic Regression Model
21.5 Example
21.6 Testing for the Significance of the Model
21.7 Interpretation of the Coefficients of the Logistic Regression Model
21.8 Dichotomous Independent Variable
21.9 Polytomous Independent Variable
21.10 Continuous Independent Variable
21.11 Multivariate Case
References
Chapter 22: Metadata
22.1 Introduction
22.2 History/Background
22.3 Data Set Metadata
22.4 Analysis Results Metadata
22.5 Regulatory Submission Metadata
References
Chapter 23: Microarray
23.1 Introduction
23.2 What is a Microarray?
23.3 Other Array Technologies
23.4 Define Objectives of the Study
23.5 Experimental Design for Microarray
23.6 Data Extraction
23.7 Microarray Informatics
23.8 Statistical Analysis
23.9 Annotation
23.10 Pathway, GO, and Class-Level Analysis Tools
23.11 Validation of Microarray Experiments
23.12 Conclusions
References
Chapter 24: Multi-Armed Bandits, Gittins Index, and Its Calculation
24.1 Introduction
24.2 Mathematical Formulation of Multi-Armed Bandits
24.3 Off-Line Algorithms for Computing Gittins Index
24.4 On-Line Algorithms for Computing Gittins Index
24.5 Computing Gittins Index for the Bernoulli Sampling Process
24.6 Conclusion
References
Chapter 25: Multiple Comparisons
25.1 Introduction
25.2 Strong and Weak Control of the FWE
25.3 Criteria for Deciding Whether Adjustment is Necessary
25.4 Implicit Multiplicity: Two-Tailed Testing
25.5 Specific Multiple Comparison Procedures
References
Chapter 26: Multiple Evaluators
26.1 Introduction
26.2 Agreement for Continuous Data
26.3 Agreement for Categorical Data
26.4 Summary and Discussion
References
Chapter 27: Noncompartmental Analysis
27.1 Introduction
27.2 Terminology
27.3 Objectives and Features of Noncompartmental Analysis
27.4 Comparison of Noncompartmental and Compartmental Models
27.5 Assumptions of NCA and Its Reported Descriptive Statistics
27.6 Calculation Formulas for NCA
27.7 Guidelines for Performance of NCA Based on Numerical Integration
27.8 Conclusions and Perspectives
References
Further Reading
Chapter 28: Nonparametric ROC Analysis for Diagnostic Trials
28.1 Introduction
28.2 Different Aspects of Study Design
28.3 Nonparametric Models and Hypotheses
28.4 Point Estimator
28.5 Asymptotic Distribution and Variance Estimator
28.6 Derivation of the Confidence Interval
28.7 Statistical Tests
28.8 Adaptations for Cluster Data
28.9 Results of a Diagnostic Study
28.10 Summary and Final Remarks
References
Chapter 29: Optimal Biological Dose for Molecularly Targeted Therapies
29.1 Introduction
29.2 Phase I Dose-Finding Designs for Cytotoxic Agents
29.3 Phase I Dose-Finding Designs for Molecularly Targeted Agents
29.4 Discussion
References
Further Reading
Chapter 30: Over- and Underdispersion Models
30.1 Introduction
30.2 Count Dispersion Models
30.3 Count Explanatory Models
30.4 Summary and Final Remarks
References
Chapter 31: Permutation Tests in Clinical Trials
31.1 Randomization Inference—Introduction
31.2 Permutation Tests—How They Work
31.3 Normal Approximation to Permutation Tests
31.4 Analyze as You Randomize
31.5 Interpretation of Permutation Analysis Results
31.6 Summary
References
Chapter 32: Pharmacoepidemiology, Overview
32.1 Introduction
32.2 The Case-Crossover Design
32.3 Confounding Bias
32.4 Risk Functions Over Time
32.5 Probabilistic Approach for Causality Assessment
32.6 Methods Based on Prescription Data
References
Chapter 33: Population Pharmacokinetic and Pharmacodynamic Methods
33.1 Introduction
33.2 Terminology
33.3 Fixed Effects Models
33.4 Random Effects Models
33.5 Model Building and Parameter Estimation
33.6 Software
33.7 Model Evaluation
33.8 Stochastic Simulation
33.9 Experimental Design
33.10 Applications
References
Further Reading
Chapter 34: Proportions: Inferences and Comparisons
34.1 Introduction
34.2 One-Sample Case
34.3 Two Independent Samples
34.4 Note on Software
References
Chapter 35: Publication Bias
35.1 Publication Bias and the Validity of Research Reviews
35.2 Research on Publication Bias
35.3 Data Suppression Mechanisms Related to Publication Bias
35.4 Prevention of Publication Bias
35.5 Assessment of Publication Bias
35.6 Impact of Publication Bias
References
Further Reading
Chapter 36: Quality of Life
36.1 Background
36.2 Measuring Health-Related Quality of Life
36.3 Development and Validation of HRQoL Measures
36.4 Use in Research Studies
36.5 Interpretation/Clinical Significance
36.6 Conclusions
References
Chapter 37: Relative Risk Modeling
37.1 Introduction
37.2 Why Model Relative Risks?
37.3 Data Structures and Likelihoods
37.4 Approaches to Model Specification
37.5 Mechanistic Models
References
Chapter 38: Sample Size Considerations for Morbidity/Mortality Trials
38.1 Introduction
38.2 General Framework for Sample Size Calculation
38.3 Choice of Test Statistics
38.4 Adjustment of Treatment Effect
38.5 Informative Noncompliance
References
Chapter 39: Sample Size for Comparing Means
39.1 Introduction
39.2 One-Sample Design
39.3 Two-Sample Parallel Design
39.4 Two-Sample Crossover Design
39.5 Multiple-Sample One-Way ANOVA
39.6 Multiple-Sample Williams Design
39.7 Discussion
References
Chapter 40: Sample Size for Comparing Proportions
40.1 Introduction
40.2 One-Sample Design
40.3 Two-Sample Parallel Design
40.4 Two-Sample Crossover Design
40.5 Relative Risk—Parallel Design
40.6 Relative Risk—Crossover Design
40.7 Discussion
References
Chapter 41: Sample Size for Comparing Time-to-Event Data
41.1 Introduction
41.2 Exponential Model
41.3 Cox’s Proportional Hazards Model
41.4 Log-Rank Test
41.5 Discussion
References
Chapter 42: Sample Size for Comparing Variabilities
42.1 Introduction
42.2 Comparing Intrasubject Variabilities
42.3 Comparing Intersubject Variabilities
42.4 Comparing Total Variabilities
42.5 Discussion
References
Chapter 43: Screening, Models of
43.1 Introduction
43.2 What is Screening?
43.3 Why Use Modeling?
43.4 Characteristics of Screening Models
43.5 A Simple Disease and Screening Model
43.6 Analytic Models for Cancer
43.7 Simulation Models for Cancer
43.8 Model Fitting and Validation
43.9 Models for Other Diseases
43.10 Current State and Future Directions
References
Chapter 44: Screening Trials
44.1 Introduction
44.2 Design Issues
44.3 Sample Size
44.5 Analysis
44.6 Trial Monitoring
References
Chapter 45: Secondary Efficacy End Points
45.1 Introduction
45.2 Literature Review
45.3 Review of Methodology for Multiplicity Adjustment and Gatekeeping Strategies for Secondary End Points
45.4 Summary
References
Further Reading
Chapter 46: Sensitivity, Specificity, and Receiver Operator Characteristic (ROC) Methods
46.1 Evaluating a Single Binary Test Against a Binary Criterion
46.2 Evaluation of a Single Binary Test: ROC Methods
46.3 Evaluation of a Test Response Measured on an Ordinal Scale: ROC Methods
46.4 Evaluation of Multiple Different Tests
46.5 The Optimal Sequence of Tests
46.6 Sampling and Measurement Issues
46.7 Summary
References
Chapter 47: Software for Genetics/Genomics
47.1 Introduction
47.2 Data Management
47.3 Genetic Analysis
47.4 Genomic Analysis
47.5 Other
References
Further Reading
Chapter 48: Stability Study Designs
48.1 Introduction
48.2 Stability Study Designs
48.3 Criteria for Design Comparison
48.4 Stability Protocol
48.5 Basic Design Considerations
48.6 Conclusions
References
Chapter 49: Subgroup Analysis
49.1 Introduction
49.2 The Dilemma of Subgroup Analysis
49.3 Planned Versus Unplanned Subgroup Analysis
49.4 Frequentist Methods
49.5 Testing Treatment by Subgroup Interactions
49.6 Subgroup Analyses in Positive Clinical Trials
49.7 Confidence Intervals for Treatment Effects within Subgroups
49.8 Bayesian Methods
References
Chapter 50: Survival Analysis, Overview
50.1 Introduction
50.2 History
50.3 Survival Analysis Concepts
50.4 Nonparametric Estimation and Testing
50.5 Parametric Inference
50.6 Comparison with Expected Survival
50.7 The Cox Regression Model
50.8 Other Regression Models for Survival Data
50.9 Multistate Models
50.10 Other Kinds of Incomplete Observation
50.11 Multivariate Survival Analysis
50.12 Concluding Remarks
References
Chapter 51: The FDA and Regulatory Issues
51.1 Caveat
51.2 Introduction
51.3 Chronology of Drug Regulation in the United States
51.4 FDA Basic Structure
51.5 IND Application Process
51.6 Drug Development and Approval Time Frame
51.7 NDA Process
51.8 U.S. Pharmacopeia and FDA
51.9 CDER Freedom of Information Electronic Reading Room
51.10 Conclusion
Chapter 52: The Kappa Index
52.1 Introduction
52.2 The Kappa Index
52.3 Inference for Kappa via Generalized Estimating Equations
52.4 The Dependence of Kappa on Marginal Rates
52.5 General Remarks
References
Chapter 53: Treatment Interruption
53.1 Introduction
53.2 Therapeutic TI Studies in HIV/AIDS
53.3 Management of Chronic Disease
53.4 Analytic Treatment Interruption in Therapeutic Vaccine Trials
53.5 Randomized Discontinuation Designs
53.6 Final Comments
References
Chapter 54: Trial Reports: Improving Reporting, Minimizing Bias, and Producing Better Evidence-Based Practice
54.1 Introduction
54.2 Reporting Issues in Clinical Trials
54.3 Moral Obligation to Improve the Reporting of Trials
54.4 Consequences of Poor Reporting of Trials
54.5 Distinguishing Between Methodological and Reporting Issues
54.6 One Solution to Poor Reporting: CONSORT 2010 and CONSORT Extensions
54.7 Impact of CONSORT
54.8 Guidance for Reporting Randomized Trial Protocols: SPIRIT
54.9 Trial Registration
54.10 Final Thoughts
References
Chapter 55: U.S. Department of Veterans Affairs Cooperative Studies Program
55.1 Introduction
55.2 History of the Cooperative Studies Program (CSP)
55.3 Organization and Functioning of the CSP
55.4 Roles of the Biostatistician and Pharmacist in the CSP
55.5 Ongoing and Completed Cooperative Studies (1972–2000)
55.6 Current Challenges and Opportunities
55.7 Concluding Remarks
References
Chapter 56: Women’s Health Initiative: Statistical Aspects and Selected Early Results
56.1 Introduction
56.2 WHI Clinical Trial and Observational Study
56.3 Study Organization
56.4 Principal Clinical Trial Comparisons, Power Calculations, and Safety and Data Monitoring
56.5 Biomarkers and Intermediate Outcomes
56.6 Data Management and Computing Infrastructure
56.7 Quality Assurance Program Overview
56.8 Early Results from the WHI Clinical Trial
56.9 Summary and Discussion
References
Chapter 57: World Health Organization (WHO): Global Health Situation
57.1 Introduction
57.2 Program Activities to the End of the Twentieth Century
57.3 Vision for the Use and Generation of Data in the First Quarter of the Twenty-First Century
Reference
Further Reading
Index
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