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.
General
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