Heterogeneity - Meta-Analysis Workshop Online Video Series Course

Описание: This video is part of a video series on #metaanalysis presented by Dr. Borenstein. The series is available for purchase and viewing through our website at:
https://meta-analysis-workshops.com/p...

In most meta-analyses the effect size varies from study to study. We would all agree that it’s important to understand how much the effect size varies, and to consider the clinical or substantive implications of this variation. In practice, however, this is rarely done. The vast majority of meta-analyses focus on the mean effect size, while little attention is paid to the dispersion in effects. This is because researchers don’t understand how much the effect size varies. In fact, the statistics typically reported for heterogeneity don’t actually tell us how much the effect size varies. For example, the most common index for reporting heterogeneity is the I-squared index, with I-squared values of 25%, 50%, and 75% often assumed to reflect low, moderate, and high levels of heterogeneity. While this use of I-squared is widespread, it is nevertheless incorrect. A meta-analysis where I-squared is 25% could have substantial variation in effects, while a meta-analysis where I-squared is 75% could have only trivial variation in effects. In fact, I present examples where this is true.

In this module I start by reviewing how we think about heterogeneity in a primary study. Then I show that the same ideas apply in a meta-analysis. In a section called “Forget what you know” I show that most of what researchers “know” about heterogeneity is wrong. Statistics such as the Q-value, the p-value, I-squared and Tau-squared, do not tell us how much the effect size varies. Then I discuss the statistics that do actually tell us how much the effect size varies – these include Tau (in some cases) and the prediction interval. I show how to compute and report these values. I then discuss how to use the heterogeneity, in conjunction with the mean effect size, to consider the clinical utility of the treatment or (more generally) the substantive implications of the findings. I also discuss what the other statistics do tell us. The module ends with an appendix that shows how the various statistics are related to each other, using clear and intelligent graphics.

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