发帖
雷达卡
Albert Vexler, Alan D. Hutson, Xiwei Chen
Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making, ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments.
The book is organized primarily based on the type of questions to be answered by inference procedures or according to the general type of mathematical derivation. It establishes the theoretical framework for each method, with a substantial amount of chapter notes included for additional reference. It then focuses on the practical application for each concept, providing real-world examples that can be easily implemented using corresponding statistical software code in R and SAS. The book also explains the basic elements and methods for constructing correct and powerful statistical decision-making processes to be adapted for complex statistical applications.
With techniques spanning robust statistical methods to more computationally intensive approaches, this book shows how to apply correct and efficient testing mechanisms to various problems encountered in medical and epidemiological studies, including clinical trials. Theoretical statisticians, medical researchers, and other practitioners in epidemiology and clinical research will appreciate the book’s novel theoretical and applied results. The book is also suitable for graduate students in biostatistics, epidemiology, health-related sciences, and areas pertaining to formal decision-making mechanisms.
Features
• Provides readers with a well-rounded understanding of the underpinnings of appropriate statistical inference
• Describes statistical strategies for handling multiplicity issues that arise in hypothesis testing across medical and epidemiological studies
• Presents new theoretical methods, open problems, new procedures, and new results related to statistical testing and its applications
• Compares different statistical methods based on real medical data
• Offers easily accessible software routines for a majority of the methods based on the R and SAS statistical software packages
Table of Contents
Preliminaries: Welcome to the Statistical Inference Club: Some Basic Concepts in Experimental Decision Making
Overview: Essential Elements of Defining Statistical Hypotheses and Constructing Statistical Tests
Errors Related to the Statistical Testing Mechanism
p-Values
Components for Constructing Test Procedures
Parametric Approach and Modeling
Warning and Advice: Limitations of Parametric Approaches: Detour to Nonparametric Approaches
Large Sample Approximate Tests
Confidence Intervals
When All Else Fails: Bootstrap?
Permutation Testing versus Bootstrap Methodology
Remarks
Atlas of the Book
Statistical Software: R and SAS
Introduction
R Software
SAS Software
Supplement
Statistical Graphics
Introduction
Descriptive Plots of Raw Data
Empirical Distribution Function Plot
Two-Sample Comparisons
Probability Plots as Informal Auxiliary Information to Inference
Heat Maps
Concluding Remarks
Supplement
A Brief Ode to Parametric Likelihood
Introduction
Likelihood Ratio Test and Its Optimality
Likelihood Ratio Based on the Likelihood Ratio Test Statistic Is the Likelihood Ratio Test Statistic
Maximum Likelihood: Is It the Likelihood?
Supplement
Appendix
Tests on Means of Continuous Data
Introduction
Univariate and p-Dimensional Likelihood Ratio Tests of Location Given Normally Distributed Data
t-Type Tests
Exact Likelihood Ratio Test for Equality of Two Normal Populations
Supplement
Empirical Likelihood
Introduction
Classical Empirical Likelihood Methods
Techniques for Analyzing Empirical Likelihoods
Density-Based Empirical Likelihood Methods
Combining Likelihoods to Construct Composite Tests and Incorporate the Maximum Data-Driven Information
Bayesians and Empirical Likelihood: Are They Mutually Exclusive?
Three Key Arguments That Support the Empirical Likelihood Methodology as a Practical Statistical Analysis Tool
Supplement
Appendix
Bayes Factor–Based Test Statistics
Introduction
Representative Values
Integrated Most Powerful Tests
Bayes Factor
Remarks
Supplement
Appendix
The Fundamentals of Receiver Operating Characteristic Curve Analyses
Introduction
ROC Curve Inference
Area under the ROC Curve
ROC Analysis and Logistic Regression: Comparison and Overestimation
Best Combinations Based on Values of Multiple Biomarkers
Notes Regarding Treatment Effects
Supplement
Appendix
Nonparametric Comparisons of Distributions
Introduction
Wilcoxon Rank-Sum Test
Kolmogorov–Smirnov Two-Sample Test
Density-Based Empirical Likelihood Ratio Tests
Density-Based Empirical Likelihood Ratio Based on Paired Data
Multiple-Group Comparison
Supplement
Appendix
Dependence and Independence: Structures, Testing, and Measuring
Introduction
Tests of Independence
Indices of Dependence
Structures of Dependence
Monte Carlo Comparisons of Tests of Independence
Data Examples
Discussion
Supplement
Goodness-of-Fit Tests (Tests for Normality)
Introduction
Shapiro–Wilk Test for Normality
Supplement
Statistical Change-Point Analysis
Introduction
Common Change-Point Models
Simple Change-Point Model
Epidemic Problems
Problems in Regression Models
Supplement
Appendix
A Brief Review of Sequential Testing Methods
Introduction
Two-Stage Designs
Sequential Probability Ratio Test
Group-Sequential Tests
Adaptive Sequential Designs
Futility Analysis
Postsequential Analysis
Supplement
Appendix: Determination of Sample Sizes Based on the Errors' Control of SPRT
A Brief Review of Multiple Testing Problems in Clinical Experiments
Introduction
Definitions of Error Rates
Power Evaluation
Remarks
Supplement
Some Statistical Procedures for Biomarker Measurements Subject to Instrumental Limitations
Introduction
Methods
Monte Carlo Experiments
Concluding Remarks
Supplement
Calculating Critical Values and p-Values for Exact Tests
Introduction
Methods of Calculating Critical Values of Exact Tests
Available Software Packages
Supplement
Appendix
Bootstrap and Permutation Methods
Introduction
Resampling Data with Replacement in SAS
Theoretical Quantities of Interest
Bootstrap Confidence Intervals
Simple Two-Group Comparisons
Simple Regression Modeling
Relationship between Empirical Likelihood and Bootstrap Methodologies
Permutation Tests
Supplement
Appendix: Bootstrap-t Example Macro
References
Author Index
Subject Index
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