Statistical Meta-Analysis with Applications

This book combines the authors' experiences on the topic and brings out a wealth of new information relevant to the study of meta-analysis. Applications ranging from business to education to environment to health sciences in both univariate and multivariate cases are presented alongside and subservient to theory. The treatment of the common mean of univariate normal populations, tests of homogeneity, one-way random effects model, categorical data, recovery of inter-block information, and combination of polls is entirely new. A special feature includes the incorporation of detailed discussions on the computational aspects and related software to carry out statistical meta-analysis in practice. Most of the methods described can be understood and applied with a solid master's level background in statistics. Practitioners will benefit from the book owing to a host of worked-out examples from numerous and various contexts.

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
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Multiple-endpoint and multiple-treatment studies
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The Bayesian approach to meta-analysis
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Publication bias
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Vote counting procedures
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Methods for combining individual tests and combining individual estimates
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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.


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