News:course: Meta-analysis in R
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13 months ago
carlopecoraro2 ★ 2.4k

Dear all,

registration is now open for the 2nd edition of the Physalia course "Meta-analyisis in R".

Dates: (online) March 6th-9th

Course website:

This course provides an overview and introduction to modern methods for meta-analysis.This course will include a mix of lectures, hands-on tutorials, and practice exercises with analyzing real meta-analytic datasets. The emphasis throughout the course is on the application of the various methods and the interpretation of the results. Analyses will be conducted using the free software R and packages metafor and psychmeta.

  • We begin with an overview of the systematic review and meta-analysis process, including problem specification, search methods, data extraction, quality evaluation, statistical analysis, model interpretation, and critique, and results in presentation.

  • Next, we will examine the parameter estimation approach to statistical analysis (effect sizes, confidence/uncertainty intervals) and explore how this approach can be used to quantify the results of individual studies and the whole literature.

  • Next, we will explore how statistical artifacts, such as sampling error, measurement error, and bias, can create the illusion of inconsistency in findings across studies.

  • Following these basic principles, we will explore methods for statistically cumulating findings across studies to reduce biases using random-effects meta-analysis and meta-regression.

  • We will examine methods for moderator analysis (stratified subgroups, meta-regression), interpretation of average effects and heterogeneity, and corrections for numerous statistical artifacts (sampling error, measurement error, range restriction, and selection effects).

  • Throughout, we will consider examples for how to interpret results and present them using tables and data visualization.

    • Next, we will examine methods for model diagnostics and sensitivity analyses related to outliers and publication bias. We will explore modern methods for detection and quantification of publication bias, as well as consideration of problems associated with older approaches.
  • Finally, we discuss meta-analysis as part of the broader systematic review process and introduce principles for planning and carrying a systematic review in a reliable, transparent, and reproducible manner. Resources for planning a systematic review and extracting results from studies will be provided.

For more information, please have a look at:

Statistics MetaAnalysis R • 657 views

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