Introduction to meta-analysis
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This document introduces meta-analysis, which combines data from multiple studies to better estimate the true effect of an intervention or exposure. It explains that meta-analysis directly analyzes effect sizes rather than p-values and statistically synthesizes all effects. Finally, it notes that publication bias, where negative results are less likely to be published, can be identified using a funnel plot in a meta-analysis.
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Introduction to meta-analysis
- 1. Introduction to Meta-analysis Youngjun Na, PhD Github: https://github.com/youngjunna Email: ruminoreticulum@gmail.com
- 2. A three minute primer on meta-analysis ▪ https://www.youtube.com/watch?v=i675gZNe3MY
- 3. What is meta-analysis? ▪ Meta‐analysis is the process of combining data from multiple studies with the goal of better estimating the true effect of a specific intervention or exposure on a particular outcome.
- 4. Why performed a meta-analysis? ▪ Because the narrative review is based on discrete reports from a series of studies, ▪ it provides no real mechanism for synthesizing the data. ▪ By contrast, in a meta-analysis we introduce two fundamental changes. ▪ 1) Meta-analysis work directly with the effect size from each study rather than the p-value. ▪ 2) Meta-analysis include all of the effects in a single statistical synthesis.
- 5. Effect size (ES) = Standardized Mean Difference (SMD)
- 6. Forest plot ▪ Forest plot is a graphical display of estimated results from a meta-analysis.
- 8. Publication bias ▪ Publication bias is a type of bias that occurs in published academic research. ▪ If the result showed positive effect -> easy to publication ▪ BUT, If the result showed negative effect -> NOT easy to publication -> remains the data in the hard-disk… orz ▪ It can be determined using funnel plot.
- 10. Thank you Q&A