Meta-Analysis Resources

Tools for Those Who Summarize the Evidence Base

Resources and networking for those who conduct or interpret meta-analyses related to any phenomenon that is gauged in multiple studies.

Tania B. Huedo-Medina will teach Meta-Analysis Using R June 5-6, 2014, as part of the DATIC series at UCONN. Here's the text describing her seminar:

Over the past 40 years, research exploring the links between biological, psychosocial, behavioral, and educational factors has grown exponentially. Understanding and reporting these interrelated factors that underlie individual variability will result in the most accurate evidence-based knowledge over time and across a variety of populations and intervention characteristics. To summarize these large masses of evidence, meta-analytic methods have seen sharply increased use across sciences, including many topics ranging from medical to social and behavioral sciences.  As a consequence, the statistical techniques to conduct meta-analysis have been extended and improved intensely in the last 10 years to support an efficient and valid practice of meta-analysis.

This workshop is a comprehensive introduction to those advanced statistical methods that have been developed recently in meta-analysis. At the end of the two days, you will be able to run meta-analytic methods under different statistical approaches that can capture and test complex models using meta-analysis.

Some experience with R and in systematic reviews or pooled analysis would be helpful.

Day 1:
The first day we will go through the basic statistical models in meta-analysis and the different statistical approaches that can be used to analyzed the individual effect sizes and the moderator effects.

  • Fixed vs. Random Effects
  • Meta-regression and subgroup analysis
  • Meta-analytic SEM
  • Multilevel meta-analysis
  • Bayesian meta-analysis

Day 2:
The second day will include more advanced methods for meta-analysis integrating certain type of designs. This day we will go through applications using the R project reviewing the different approaches conceptually and practically.

  • Single-case design meta-analysis
  • Network meta-analysis
  • IPD vs. AD meta-analysis
  • Applications with R

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