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· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9781439861424
· 쪽수 : 438쪽
· 출판일 : 2014-10-13
목차
List of Figures
List of Tables
Preface to the Third Edition
Introduction
What Is a Cross-Over Trial?
With Which Sort of Cross-Over Trial Are We Concerned?
Why Do Cross-Over Trials Need Special Consideration?
A Brief History
Notation, Models, and Analysis
Aims of This Book
Structure of the Book
The 2×2 Cross-Over Trial
Introduction
Plotting the Data
Analysis Using T-Tests
Sample Size Calculations
Analysis of Variance
Aliasing of Effects
Consequences of Preliminary Testing
Analyzing the Residuals
A Bayesian Analysis of the 2×2 Trial
Bayes Using Approximations
Bayes Using Gibbs Sampling
Use of Baseline Measurements
Use of Covariates
Nonparametric Analysis
Testing λ1 =λ2
Testing t1 =t2, Given that λ1 =λ2
Testing π1 =π2, Given that λ1 =λ2
Obtaining the Exact Version of the Wilcoxon Ranksum Test Using Tables
Point Estimate and Confidence Interval for Δ =t1 ?t2
A More General Approach to Nonparametric Testing
Nonparametric Analysis of Ordinal Data
Analysis of a Multicenter Trial
Tests Based on Nonparametric Measures of Association
Binary Data
Introduction
McNemar’s Test
The Mainland?Gart Test
Fisher’s Exact Version of the Mainland?Gart Test
Prescott’s Test
Higher-Order Designs for Two Treatments
Introduction
"Optimal" Designs
Balaam’s Design for Two Treatments
Effect of Preliminary Testing in Balaam’s Design
Three-Period Designs with Two Sequences
Three-Period Designs with Four Sequences
A Three-Period Six-Sequence Design
Which Three-Period Design to Use?
Four-Period Designs with Two Sequences
Four-Period Designs with Four Sequences
Four-Period Designs with Six Sequences
Which Four-Period Design to Use?
Which Two-Treatment Design to Use?
Designing Cross-Over Trials
Introduction
Variance-Balanced Designs
Designs with p = t
Designs with p < t
Designs with p > t
Designs with Many Periods
Optimality Results for Cross-Over Designs
Which Variance-Balanced Design to Use?
Partially Balanced Designs
Comparing Test Treatments to a Control
Factorial Treatment Combinations
Extending the Simple Model for Carry-Over Effects
Computer Search Algorithms
Analysis of Continuous Data
Introduction
Example: INNOVO Trial: Dose?Response Study
Fixed Subject Effects Model
Ignoring the Baseline Measurements
Adjusting for Carry-Over Effects
Random Subject Effects Model
Random Subject Effects
Recovery of Between-Subject Information
Small Sample Inference with Random Effects
Missing Values
Use of Baseline Measurements
Introduction and Examples
Notation and Basic Results
Pre-Randomization Covariates
Period-Dependent Baseline Covariates
Baselines as Response Variables
Incomplete Data
Analyses for Higher-Order Two-Treatment Designs
Analysis for Balaam’s Design
General Linear Mixed Model
Analysis of Repeated Measurements within Periods
Example: Insulin Mixtures
Cross-Over Data as Repeated Measurements
Allowing More General Covariance Structures
Robust Analyses for Two-Treatment Designs
Higher-Order Designs
Case Study: An Analysis of a Trial with Many Periods
Example: McNulty’s Experiment
McNulty’s Analysis
Fixed Effects Analysis
Random Subject Effects and Covariance Structure
Modeling the Period Effects
Analysis of Discrete Data
Introduction
Modeling Dependent Categorical Data
Types of Model
Binary Data: Subject Effect Models
Dealing with the Subject Effects
Conditional Likelihood
Binary Data: Marginal Models
Marginal Model
Categorical Data
Example: Trial on Patients with Primary Dysmenorrhea
Types of Model for Categorical Outcomes
Subject Effects Models
Marginal Models
Further Topics
Count Data
Time to Event Data
Issues Associated with Scale
Bioequivalence Trials
What Is Bioequivalence?
Testing for Average Bioequivalence
Case Study: Phase I Dose?Response Noninferiority Trial
Introduction
Model for Dose Response
Testing for Noninferiority
Choosing Doses for the Fifth Period
Analysis of the Design Post-Interim
Case Study: Choosing a Dose?Response Model
Introduction
Analysis of Variance
Dose?Response Modeling
Case Study: Conditional Power
Introduction
Variance Spending Approach
Interim Analysis of Sleep Trial
Case Study: Proof of Concept Trial with Sample Size Re-Estimation
Introduction
Calculating the Sample Size
Interim Analysis
Data Analysis
Case Study: Blinded Sample Size Re-Estimation in a Bioequivalence Study
Introduction
Blinded Sample Size Re-Estimation (BSSR)
Example
Case Study: Unblinded Sample Size Re-Estimation in a Bioequivalence Study That Has a Group Sequential Design
Introduction
Sample Size Re-Estimation in a Group Sequential Design
Modification of Sample Size Re-Estimation in a Group Sequential Design
Case Study: Various Methods for an Unblinded Sample Size Re-Estimation in a Bioequivalence Study
Introduction
Methods
Example
Appendix A: Least Squares Estimation
Case 1
Case 2
Case 3
Bibliography
Index
