Hartung J., Knapp G., Sinha B.K. Statistical Meta-Analysis with Applications

Hoboken: John Wiley & Sons, Inc., 2008. — 247 p.An accessible introduction to performing meta-analysis across various areas of research.
The practice of meta-analysis allows researchers to obtain findings from various studies and compile them to verify and form one overall conclusion. Statistical Meta-Analysis with Applications presents the necessary statistical methodologies that allow readers to tackle the four main stages of meta-analysis: problem formulation, data collection, data evaluation, and data analysis and interpretation. Combining the authors' expertise on the topic with a wealth of up-to-date information, this book successfully introduces the essential statistical practices for making thorough and accurate discoveries across a wide array of diverse fields, such as business, public health, biostatistics, and environmental studies.Two main types of statistical analysis serve as the foundation of the methods and techniques: combining tests of effect size and combining estimates of effect size. Additional topics covered include:Meta-analysis regression procedures;
Multiple-endpoint and multiple-treatment studies;
The Bayesian approach to meta-analysis;
Publication bias;
Vote counting procedures;
Methods for combining individual tests and combining individual estimates;
Using meta-analysis to analyze binary and ordinal categorical data.Numerous worked-out examples in each chapter provide the reader with a step-by-step understanding of the presented methods. All exercises can be computed using the R and SAS software packages, which are both available via the book's related Web site. Extensive references are also included, outlining additional sources for further study.Requiring only a working knowledge of statistics, Statistical Meta-Analysis with Applications is a valuable supplement for courses in biostatistics, business, public health, and social research at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians working in industry, academia, and government.Introduction
Various Measures of Effect Size
Effect Size Based on Means
Effect Size Based on Proportions
Effect Size Based on ϕ Coefficient and Odds Ratio
Effect Size Based on Correlation
Combining Independent TestsDescription of Combined Tests
Methods of Combining Effect Sizes
Inference about a Common Mean of Several Univariate Normal Populations
Asymptotic Comparison of Some Estimates of Common Mean for k = 2 Populations
Confidence Intervals for the Common MeanResults on Common Mean Estimation
Applications
Appendix: Theory of Fisher’s Method
Tests of Homogeneity in Meta-Analysis
Model and Test Statistics
An Exact Test of Homogeneity
Applications
One-Way Random Effects ModelHomogeneous Error Variances
Heterogeneous Error Variances
Combining Controlled Trials with Normal Outcomes
Difference of Means
Standardized Difference of Means
Ratio of Means
Combining Controlled Trials with Discrete Outcomes
Binary Data
Ordinal Data
Meta-Regression
Model with One Covariate
Model with More Than One Covariate
Further Extensions and Applications
Multivariate Meta-Analysis
Combining Multiple Dependent Variables from a Single Study
Modeling Multivariate Effect Sizes
Bayesian Meta-Analysis
A General Bayesian Model for Meta-Analysis under Normality
Further Examples of Bayesian Analyses
A Unified Bayesian Approach to Meta-Analysis
Further Results on Bayesian Meta-Analysis
Publication Bias
Recovery of Interblock Information
Notation and Test Statistics
BIBD with Fixed Treatment Effects
Combination of Polls
Formulation of the Problem
Meta-Analysis of Polls
Vote Counting Procedures
Computational Aspects
Extracting Summary Statistics
Combining Tests
Generalized P-values
Combining Effect Sizes
Data Sets
Validity Studies
Effects of Teacher Expectance on Pupil IQ
Dentifrice Data
Effectiveness of Amlodipine onWork Capacity
Effectiveness of Cisapride on the Treatment of Nonulcer Dyspepsia
Second-hand Smoking
Effectiveness of Misoprostol in Preventing Gastrointestinal Damage
Prevention of Tuberculosis