ABSTRACT

Meta-analysis is the application of statistics to combine results from multiple studies and draw appropriate inferences. Its use and importance have exploded over the last 25 years as the need for a robust evidence base has become clear in many scientific areas, including medicine and health, social sciences, education, psychology, ecology, and economics.

Recent years have seen an explosion of methods for handling complexities in meta-analysis, including explained and unexplained heterogeneity between studies, publication bias, and sparse data. At the same time, meta-analysis has been extended beyond simple two-group comparisons of continuous and binary outcomes to comparing and ranking the outcomes from multiple groups, to complex observational studies, to assessing heterogeneity of effects, and to survival and multivariate outcomes. Many of these methods are statistically complex and are tailored to specific types of data.

Key features

  • Rigorous coverage of the full range of current statistical methodology used in meta-analysis
  • Comprehensive, coherent, and unified overview of the statistical foundations behind meta-analysis
  • Detailed description of the primary methods for both univariate and multivariate data
  • Computer code to reproduce examples in chapters
  • Thorough review of the literature with thousands of references
  • Applications to specific types of biomedical and social science data
  • Supplementary website with code, data, sample chapters, and errata

This book is for a broad audience of graduate students, researchers, and practitioners interested in the theory and application of statistical methods for meta-analysis. It is written at the level of graduate courses in statistics, but will be of interest to and readable for quantitative scientists from a range of disciplines. The book can be used as a graduate level textbook, as a general reference for methods, or as an introduction to specialized topics using state-of-the art methods.

chapter 1|18 pages

Introduction to Systematic Review and Meta-Analysis

ByChristopher H. Schmid, Ian R. White, Theo Stijnen

chapter 2|8 pages

General Themes in Meta-Analysis

ByChristopher H. Schmid, Theo Stijnen, Ian R. White

chapter 3|14 pages

Choice of Effect Measure and Issues in Extracting Outcome Data

ByIan R. White, Christopher H. Schmid, Theo Stijnen

chapter 4|24 pages

Analysis of Univariate Study-Level Summary Data Using Normal Models

ByTheo Stijnen, Ian R. White, Christopher H. Schmid

chapter 5|26 pages

Exact Likelihood Methods for Group-Based Summaries

ByTheo Stijnen, Christopher H. Schmid, Martin Law, Dan Jackson, Ian R. White

chapter 6|38 pages

Bayesian Methods for Meta-Analysis

ByChristopher H. Schmid, Bradley P. Carlin, Nicky J. Welton

chapter 7|22 pages

Meta-Regression

ByJulian P.T. Higgins, Jose A. López-López, Ariel M. Aloe

chapter 8|12 pages

Individual Participant Data Meta-Analysis

ByLesley Stewart, Mark Simmonds

chapter 9|24 pages

Multivariate Meta-Analysis

ByDan Jackson, Ian R. White, Richard D. Riley

chapter 10|32 pages

Network Meta-Analysis

ByAdriani Nikolakopoulou, Ian R. White, Georgia Salanti

chapter 11|36 pages

Model Checking in Meta-Analysis

ByWolfgang Viechtbauer

chapter 12|28 pages

Handling Internal and External Biases:

Quality and Relevance of Studies
ByRebecca M. Turner, Nicky J. Welton, Hayley E. Jones, Jelena Savović

chapter 13|30 pages

Publication and Outcome Reporting Bias

ByArielle Marks-Anglin, Rui Duan, Yong Chen, Orestis Panagiotou, Christopher H. Schmid

chapter 14|16 pages

Control Risk Regression

ByAnnamaria Guolo, Christopher H. Schmid, Theo Stijnen

chapter 15|18 pages

Multivariate Meta-Analysis of Survival Proportions

ByMarta Fiocco

chapter 16|24 pages

Meta-Analysis of Correlations, Correlation Matrices, and Their Functions

ByBetsy Jane Becker, Ariel M. Aloe, Michael W.-L. Cheung

chapter 17|24 pages

The Meta-Analysis of Genetic Studies

ByCosetta Minelli, John Thompson

chapter 18|34 pages

Meta-Analysis of Dose-Response Relationships

ByNicola Orsini, Donna Spiegelman

chapter 19|28 pages

Meta-Analysis of Diagnostic Tests

ByYulun Liu, Xiaoye Ma, Yong Chen, Theo Stijnen, Haitao Chu

chapter 20|22 pages

Meta-Analytic Approach to Evaluation of Surrogate Endpoints

ByTomasz Burzykowski, Marc Buyse, Geert Molenberghs, Ariel Alonso, Wim Van der Elst, Ziv Shkedy

chapter 21|24 pages

Meta-Analysis of Epidemiological Data, with a Focus on Individual Participant Data

ByAngela Wood, Stephen Kaptoge, Michael Sweeting, Clare Oliver-Williams

chapter 22|20 pages

Meta-Analysis of Prediction Models

ByEwout Steyerberg, Daan Nieboer, Thomas Debray, Hans van Houwelingen

chapter 23|21 pages

Using Meta-Analysis to Plan Further Research

ByClaire Rothery, Susan Griffin, Hendrik Koffijberg, Karl Claxton