Meta Analysis
Meta-analysis is a statistical method that combines effect sizes from multiple independent studies into a weighted pooled estimate and quantifies how much the effects vary across studies.
메타분석은 여러 독립 연구의 효과크기를 가중 결합하여 통합 추정치를 산출하고 연구 간 효과의 이질성을 정량화하는 통계적 방법이다.
How do I combine results from several studies into one overall effect?
여러 연구 결과를 하나의 전체 효과로 어떻게 통합하나요?
Meta-analysis weights each study's effect size by its precision, usually inverse variance, to produce a pooled estimate under a fixed-effect or random-effects model and reports heterogeneity statistics. It fits systematic reviews where comparable effect sizes and their standard errors or variances can be extracted from each study.
When to use Meta Analysis
- Effect sizes and variances (or standard errors) extracted from several studies
- Systematic reviews estimating an overall treatment effect or association
- Exploring whether study characteristics explain differences in effects (meta-regression)
When to use something else
- Raw individual-level data from all studies are available: use individual participant data meta-analysis or multilevel models
- Several treatments must be compared across a network of trials: use network meta-analysis
Assumptions and requirements
- Effect sizes measure the same construct on a common metric
- Studies are independent, or dependence is modeled
- Random-effects model when true effects are expected to vary across studies
- Publication bias assessed rather than assumed absent
What the analysis reports
- Pooled effect size with confidence interval and p-value
- Heterogeneity statistics: Q, I², and tau²
- Forest plot
- Funnel plot and tests such as Egger's test
- Prediction interval for the effect in a new study
How to report it
A random-effects meta-analysis of [k] studies yielded a pooled effect of [value], 95% CI [[value], [value]], p = [value], with heterogeneity I² = [value]%, τ² = [value].
Related methods
- Meta-Regression
- Network Meta-Analysis
- Publication Bias Tests
Run Meta Analysis on Quorant
- Data you need
- a table (CSV or Excel) with an outcome variable and predictor (feature) columns
- Price
- 250 credits for one analysis up to 10,000 rows (complexity level 2); larger or wider data costs more. Optional methodologist review: 1200 credits.
- Checking
- Python results are cross-checked against R reference outputs in every deployment
- Before you pay
- A free pre-analysis diagnosis checks your data and gives an exact quote; nothing is deducted until you approve it.