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Methods and Applications of Statistics in Clinical Trials, Volume 2: Planning, Analysis, and Inferential Methods

by N. Balakrishnan

June 2014

Intermediate to advanced

960 pages

36h 34m

English

Wiley

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1.1 Introduction1.2 Overdispersed Binomial and Count Models1.3 Other Approaches to Account for Overdispersion1.4 Underdispersion1.5 Software NotesReferences
2.1 Introduction2.2 Factors, Levels, Effects, and Cells2.3 Cell Means Model2.4 One-Way Classification2.5 Parameter Estimation2.6 The R(.) Notation—Partitioning Sum of Squares2.7 ANOVA—Hypothesis of Equal Means2.8 Multiple Comparisons2.9 Two-Way Crossed Classification2.10 Balanced and Unbalanced Data2.11 Interaction Between Rows and Columns2.12 Analysis of Variance TableReferences
3.1 Introduction3.2 Choice of HRQOL Instruments3.3 Establishment of Clear Objectives in HRQOL Assessments3.4 Methods for HRQOL Assessment3.5 HRQOL as the Primary End Point3.6 Interpretation of HRQOL Results3.7 Examples3.8 ConclusionReferencesFurther Reading
4.1 Introduction4.2 Exploration Versus Exploitation with Complete Observations4.3 Exploration Versus Exploitation with Censored Observations4.4 ConclusionReferences

5.1 Introduction5.2 A Bayesian Decision-Theoretic Design5.3 An Example of Dose Escalation in Healthy Volunteer Studies5.4 DiscussionReferences
6.1 Introduction6.2 Plug-In Principle6.3 Monte Carlo Sampling— The “Second Bootstrap Principle”6.4 Bias and Standard Error6.5 Examples6.6 Model Stability6.7 Accuracy of Bootstrap Distributions6.8 Bootstrap Confidence Intervals6.9 Hypothesis Testing6.10 Planning Clinical Trials6.11 How Many Bootstrap Samples Are Needed6.12 Additional ReferencesReferences
7.1 Introduction7.2 Conditional Power7.3 Weight-Averaged Conditional Power or Bayesian Predictive Power7.4 Conditional Power of a Different Kind: Discordance Probability7.5 Analysis of a Randomized Trial7.6 Conditional Power: Pros and ConsReferences
8.1 Introduction8.2 Definitions and Design Issues8.3 Cost and Effectiveness Data8.4 The Analysis of Costs and Outcomes8.5 Robustness and Generalizability in Cost-Effectiveness AnalysisReferencesFurther Reading
9.1 Introduction9.2 Cox Model for Univariate Failure Time Data Analysis9.3 Marginal Models for Multivariate Failure Time Data Analysis9.4 Practical Issues in Using the Cox Model9.5 Examples9.6 Extensions9.7 Softwares and CodesReferencesFurther Reading
10.1 Introduction10.2 Classical EL: Several Ingredients for Theoretical Evaluations10.3 The Relationship Between Empirical Likelihood and Bootstrap Methodologies10.4 Bayes Methods Based on Empirical Likelihoods10.5 Mixtures of Likelihoods10.6 An Example: ROC Curve Analyses Based on Empirical Likelihoods10.7 Applications of Empirical Likelihood Methodology in Clinical Trials or Other Data Analyses10.8 Concluding RemarksAppendixReferences
11.1 Introduction11.2 Univariate Frailty Models11.3 Multivariate Frailty Models11.4 SoftwareReferences
12.1 Introduction12.2 Common Statistical Approaches to Futility Monitoring12.3 Examples12.4 DiscussionReferencesFurther Reading
13.1 Introduction13.2 Advances in Medical Imaging13.3 Evolutionary Developments in Imaging13.4 ConclusionReferences
14.1 Introduction to Medical Imaging: Different Ways of Creating Visible Contrast Among Tissues14.2 What the Body Does to the X-Ray Beam: Subject Contrast From Differential Attenuation of the X-Ray Beam by Various Tissues14.3 What the X-Ray Beam Does to the Body: Known Medical Benefits Versus Possible Radiogenic Risks14.4 Capturing the Visual Image: Analog (20th Century) X-Ray Image Receptors
15.1 The Computer in Medical Imaging15.2 The Digital Planar X-Ray Modalities: Computed Radiography and Digital Radiography and Fluoroscopy15.3 Digital Fluoroscopy and Digital Subtraction Angiography15.4 Digital Tomosynthesis: Planar Imaging in Three Dimensions15.5 Computed Tomography: Superior Contrast in Three-Dimensional X-Ray Attenuation Maps
16.1 Introduction16.2 Missing Information16.3 The Intention-to-Treat Design16.4 Efficiency of the Intent-to-Treat Analysis16.5 Compliance-Adjusted Analyses16.6 ConclusionReferencesFurther Reading
17.1 Introduction17.2 Opportunities and Dangers of Interim Analyses17.3 The Development of Techniques for Conducting Interim Analyses17.4 Methodology for Interim Analyses17.5 An Example: Statistics for Lamivudine17.6 Interim Analyses in Practice17.7 ConclusionsReferences
18.1 Definition18.2 The Importance of Reliability in Clinical Trials18.3 How Large a Reliability Coefficient Is Large Enough?18.4 Design and Analysis of Reliability Studies18.5 Estimate of the Reliability Coefficient—Parametric18.6 Estimation of the Reliability Coefficient— Nonparametric18.7 Estimation of the Reliability Coefficient—Binary18.8 Estimation of the Reliability Coefficient—Categorical18.9 Strategies to Increase Reliability (Spearman–Brown Projection)18.10 Other Types of ReliabilitiesReferences
19.1 Introduction19.2 Intrarater Reliability for Continuous Scores19.3 Nominal Scale Score Data19.4 Ordinal and Interval Score Data19.5 Concluding RemarksReferencesFurther Reading
20.1 Introduction20.2 Estimation of Survival Function20.3 Additional TopicsReferences
21.1 Introduction21.2 Fitting the Logistic Regression Model21.3 The Multiple Logistic Regression Model21.4 Fitting the Multiple Logistic Regression Model21.5 Example21.6 Testing for the Significance of the Model21.7 Interpretation of the Coefficients of the Logistic Regression Model21.8 Dichotomous Independent Variable21.9 Polytomous Independent Variable21.10 Continuous Independent Variable21.11 Multivariate CaseReferences
22.1 Introduction22.2 History/Background22.3 Data Set Metadata22.4 Analysis Results Metadata22.5 Regulatory Submission MetadataReferences
23.1 Introduction23.2 What is a Microarray?23.3 Other Array Technologies23.4 Define Objectives of the Study23.5 Experimental Design for Microarray23.6 Data Extraction23.7 Microarray Informatics23.8 Statistical Analysis23.9 Annotation23.10 Pathway, GO, and Class-Level Analysis Tools23.11 Validation of Microarray Experiments23.12 ConclusionsReferences
24.1 Introduction24.2 Mathematical Formulation of Multi-Armed Bandits24.3 Off-Line Algorithms for Computing Gittins Index24.4 On-Line Algorithms for Computing Gittins Index24.5 Computing Gittins Index for the Bernoulli Sampling Process24.6 ConclusionReferences
25.1 Introduction25.2 Strong and Weak Control of the FWE25.3 Criteria for Deciding Whether Adjustment is Necessary25.4 Implicit Multiplicity: Two-Tailed Testing25.5 Specific Multiple Comparison ProceduresReferences
26.1 Introduction26.2 Agreement for Continuous Data26.3 Agreement for Categorical Data26.4 Summary and DiscussionReferences
27.1 Introduction27.2 Terminology27.3 Objectives and Features of Noncompartmental Analysis27.4 Comparison of Noncompartmental and Compartmental Models27.5 Assumptions of NCA and Its Reported Descriptive Statistics27.6 Calculation Formulas for NCA27.7 Guidelines for Performance of NCA Based on Numerical Integration27.8 Conclusions and PerspectivesReferencesFurther Reading
28.1 Introduction28.2 Different Aspects of Study Design28.3 Nonparametric Models and Hypotheses28.4 Point Estimator28.5 Asymptotic Distribution and Variance Estimator28.6 Derivation of the Confidence Interval28.7 Statistical Tests28.8 Adaptations for Cluster Data28.9 Results of a Diagnostic Study28.10 Summary and Final RemarksReferences
29.1 Introduction29.2 Phase I Dose-Finding Designs for Cytotoxic Agents29.3 Phase I Dose-Finding Designs for Molecularly Targeted Agents29.4 DiscussionReferencesFurther Reading
30.1 Introduction30.2 Count Dispersion Models30.3 Count Explanatory Models30.4 Summary and Final RemarksReferences
31.1 Randomization Inference—Introduction31.2 Permutation Tests—How They Work31.3 Normal Approximation to Permutation Tests31.4 Analyze as You Randomize31.5 Interpretation of Permutation Analysis Results31.6 SummaryReferences
32.1 Introduction32.2 The Case-Crossover Design32.3 Confounding Bias32.4 Risk Functions Over Time32.5 Probabilistic Approach for Causality Assessment32.6 Methods Based on Prescription DataReferences
33.1 Introduction33.2 Terminology33.3 Fixed Effects Models33.4 Random Effects Models33.5 Model Building and Parameter Estimation33.6 Software33.7 Model Evaluation33.8 Stochastic Simulation33.9 Experimental Design33.10 ApplicationsReferencesFurther Reading
34.1 Introduction34.2 One-Sample Case34.3 Two Independent Samples34.4 Note on SoftwareReferences
35.1 Publication Bias and the Validity of Research Reviews35.2 Research on Publication Bias35.3 Data Suppression Mechanisms Related to Publication Bias35.4 Prevention of Publication Bias35.5 Assessment of Publication Bias35.6 Impact of Publication BiasReferencesFurther Reading
36.1 Background36.2 Measuring Health-Related Quality of Life36.3 Development and Validation of HRQoL Measures36.4 Use in Research Studies36.5 Interpretation/Clinical Significance36.6 ConclusionsReferences
37.1 Introduction37.2 Why Model Relative Risks?37.3 Data Structures and Likelihoods37.4 Approaches to Model Specification37.5 Mechanistic ModelsReferences
38.1 Introduction38.2 General Framework for Sample Size Calculation38.3 Choice of Test Statistics38.4 Adjustment of Treatment Effect38.5 Informative NoncomplianceReferences
39.1 Introduction39.2 One-Sample Design39.3 Two-Sample Parallel Design39.4 Two-Sample Crossover Design39.5 Multiple-Sample One-Way ANOVA39.6 Multiple-Sample Williams Design39.7 DiscussionReferences
40.1 Introduction40.2 One-Sample Design40.3 Two-Sample Parallel Design40.4 Two-Sample Crossover Design40.5 Relative Risk—Parallel Design40.6 Relative Risk—Crossover Design40.7 DiscussionReferences
41.1 Introduction41.2 Exponential Model41.3 Cox’s Proportional Hazards Model41.4 Log-Rank Test41.5 DiscussionReferences
42.1 Introduction42.2 Comparing Intrasubject Variabilities42.3 Comparing Intersubject Variabilities42.4 Comparing Total Variabilities42.5 DiscussionReferences
43.1 Introduction43.2 What is Screening?43.3 Why Use Modeling?43.4 Characteristics of Screening Models43.5 A Simple Disease and Screening Model43.6 Analytic Models for Cancer43.7 Simulation Models for Cancer43.8 Model Fitting and Validation43.9 Models for Other Diseases43.10 Current State and Future DirectionsReferences
44.1 Introduction44.2 Design Issues44.3 Sample Size44.5 Analysis44.6 Trial MonitoringReferences
45.1 Introduction45.2 Literature Review45.3 Review of Methodology for Multiplicity Adjustment and Gatekeeping Strategies for Secondary End Points45.4 SummaryReferencesFurther Reading
46.1 Evaluating a Single Binary Test Against a Binary Criterion46.2 Evaluation of a Single Binary Test: ROC Methods46.3 Evaluation of a Test Response Measured on an Ordinal Scale: ROC Methods46.4 Evaluation of Multiple Different Tests46.5 The Optimal Sequence of Tests46.6 Sampling and Measurement Issues46.7 SummaryReferences
47.1 Introduction47.2 Data Management47.3 Genetic Analysis47.4 Genomic Analysis47.5 OtherReferencesFurther Reading
48.1 Introduction48.2 Stability Study Designs48.3 Criteria for Design Comparison48.4 Stability Protocol48.5 Basic Design Considerations48.6 ConclusionsReferences
49.1 Introduction49.2 The Dilemma of Subgroup Analysis49.3 Planned Versus Unplanned Subgroup Analysis49.4 Frequentist Methods49.5 Testing Treatment by Subgroup Interactions49.6 Subgroup Analyses in Positive Clinical Trials49.7 Confidence Intervals for Treatment Effects within Subgroups49.8 Bayesian MethodsReferences
50.1 Introduction50.2 History50.3 Survival Analysis Concepts50.4 Nonparametric Estimation and Testing50.5 Parametric Inference50.6 Comparison with Expected Survival50.7 The Cox Regression Model50.8 Other Regression Models for Survival Data50.9 Multistate Models50.10 Other Kinds of Incomplete Observation50.11 Multivariate Survival Analysis50.12 Concluding RemarksReferences
51.1 Caveat51.2 Introduction51.3 Chronology of Drug Regulation in the United States51.4 FDA Basic Structure51.5 IND Application Process51.6 Drug Development and Approval Time Frame51.7 NDA Process51.8 U.S. Pharmacopeia and FDA51.9 CDER Freedom of Information Electronic Reading Room51.10 Conclusion
52.1 Introduction52.2 The Kappa Index52.3 Inference for Kappa via Generalized Estimating Equations52.4 The Dependence of Kappa on Marginal Rates52.5 General RemarksReferences
53.1 Introduction53.2 Therapeutic TI Studies in HIV/AIDS53.3 Management of Chronic Disease53.4 Analytic Treatment Interruption in Therapeutic Vaccine Trials53.5 Randomized Discontinuation Designs53.6 Final CommentsReferences
54.1 Introduction54.2 Reporting Issues in Clinical Trials54.3 Moral Obligation to Improve the Reporting of Trials54.4 Consequences of Poor Reporting of Trials54.5 Distinguishing Between Methodological and Reporting Issues54.6 One Solution to Poor Reporting: CONSORT 2010 and CONSORT Extensions54.7 Impact of CONSORT54.8 Guidance for Reporting Randomized Trial Protocols: SPIRIT54.9 Trial Registration54.10 Final ThoughtsReferences
55.1 Introduction55.2 History of the Cooperative Studies Program (CSP)55.3 Organization and Functioning of the CSP55.4 Roles of the Biostatistician and Pharmacist in the CSP55.5 Ongoing and Completed Cooperative Studies (1972–2000)55.6 Current Challenges and Opportunities55.7 Concluding RemarksReferences
56.1 Introduction56.2 WHI Clinical Trial and Observational Study56.3 Study Organization56.4 Principal Clinical Trial Comparisons, Power Calculations, and Safety and Data Monitoring56.5 Biomarkers and Intermediate Outcomes56.6 Data Management and Computing Infrastructure56.7 Quality Assurance Program Overview56.8 Early Results from the WHI Clinical Trial56.9 Summary and DiscussionReferences
57.1 Introduction57.2 Program Activities to the End of the Twentieth Century57.3 Vision for the Use and Generation of Data in the First Quarter of the Twenty-First CenturyReferenceFurther Reading

Overview

Methods and Applications of Statistics in Clinical Trials, Volume 2: Planning, Analysis, and Inferential Methods includes updates of established literature from the Wiley Encyclopedia of Clinical Trials as well as original material based on the latest developments in clinical trials. Prepared by a leading expert, the second volume includes numerous contributions from current prominent experts in the field of medical research. In addition, the volume features:

Multiple new articles exploring emerging topics, such as evaluation methods with threshold, empirical likelihood methods, nonparametric ROC analysis, over- and under-dispersed models, and multi-armed bandit problems

Up-to-date research on the Cox proportional hazard model, frailty models, trial reports, intrarater reliability, conditional power, and the kappa index

Key qualitative issues including cost-effectiveness analysis, publication bias, and regulatory issues, which are crucial to the planning and data management of clinical trials

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs

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Methods and Applications of Statistics in Clinical Trials, Volume 2: Planning, Analysis, and Inferential Methods

by N. Balakrishnan

Overview

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Overview

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,and much more.

You might also like

Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs

Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods

Analysis of Clinical Trials Using SAS, 2nd Edition

Applied Modeling Techniques and Data Analysis 2

Publisher Resources

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.