Statistical Meta-Analysis with Applications

<b>An accessible introduction to performing meta-analysis across various areas of research</b> <p>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.</p> <p>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:</p> <ul> <li>Meta-analysis regression procedures</li> <li> <p>Multiple-endpoint and multiple-treatment studies</p> </li> <li> <p>The Bayesian approach to meta-analysis</p> </li> <li> <p>Publication bias</p> </li> <li> <p>Vote counting procedures</p> </li> <li> <p>Methods for combining individual tests and combining individual estimates</p> </li> <li> <p>Using meta-analysis to analyze binary and ordinal categorical data</p> </li> </ul> <p>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.</p> <p>Requiring only a working knowledge of statistics, <i>Statistical Meta-Analysis with Applications</i> 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.</p>