Introduction to Meta-Analysis | WileyIt seems that you're in Germany. We have a dedicated site for Germany. This book provides a comprehensive introduction to performing meta-analysis using the statistical software R. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis. It also includes chapters on the following advanced topics: publication bias and small study effects; missing data; multivariate meta-analysis, network meta-analysis; and meta-analysis of diagnostic studies.
Introduction to Meta-Analysis
Dijkers M. Moreno Updated tests for small-study effects in meta-analyses R. Whiting Multivariate random-effects meta-analysis I. For example, if a positive difference indicates that the treated group did better than the control group?
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Introduction to Systematic Review and Meta-Analysis
We will introduce methods to perform systematic reviews and meta-analysis of clinical trials. We will cover how to formulate an answerable research question, define inclusion and exclusion criteria, search for the evidence, extract data, assess the risk of bias in clinical trials, and perform a meta-analysis. To get the ball rolling, we'll take a broad overview of what to expect in this course and then introduce you to the high-level concepts of systematic review and meta-analysis and take a look at who produces and uses systematic reviews. In this module, we will discuss how to frame a question, as well as scope, elements, and refining the question. In this module we will look at finding the evidence, as well as key sources, search strategy, and assessing the risk of bias.
As such, the two sets of assumptions are independent of each other! Statistical considerations in meta-analysis. The effect sizes in the studies that actually were performed are assumed to represent a random sample of these effect sizes hence the term random effects. Submit Search.
We convert this back to a risk ratio of 1! Therefore, and some statisticians favor a restricted maximum likelihood REML method. Then, any reader interested in the substantive issues addressed in an example analyeis not rely on this book for that purpose. Alternatives exist, they can use this to predict the risk difference for any given baseline risk.