Meta Analysis
- Pools results from several studies to produce a single, more precise summary estimate.
- Increases effective sample size, which is useful when individual studies are small.
- Helps assess consistency across studies, explore sources of variability, and evaluate overall evidence quality.
Definition
Section titled “Definition”Meta-analysis is a statistical method used to combine the results of multiple studies in order to obtain a more precise estimate of the effect of an intervention or exposure by pooling data from different studies and using statistical techniques to calculate a summary estimate of the effect size.
Explanation
Section titled “Explanation”Meta-analysis works by identifying relevant studies on a topic, pooling their data, and applying statistical techniques to calculate a combined effect size. By aggregating results, meta-analysis often achieves a larger combined sample size than any single study, improving precision. It also enables assessment of consistency across studies, identification of discrepancies, investigation of factors that may explain variability (for example, differences in country, methods, or interventions), and evaluation of the overall quality of the evidence on a topic.
Examples
Section titled “Examples”Psychotherapy for treating depression
Section titled “Psychotherapy for treating depression”Researchers combined results from several studies of psychotherapy for depression. The meta-analysis showed that psychotherapy was effective in reducing symptoms of depression, with an overall effect size of 0.62.
Physical activity and breast cancer risk
Section titled “Physical activity and breast cancer risk”Researchers combined results from several studies on physical activity and breast cancer risk. The meta-analysis showed that physical activity was associated with a reduced risk of breast cancer, with an overall effect size of 0.46.
Use cases
Section titled “Use cases”- Obtaining a more precise estimate of an intervention or exposure effect when multiple studies exist.
- Improving precision when individual studies have small sample sizes.
- Assessing consistency and identifying discrepancies among study results.
- Exploring factors contributing to variability across studies (for example, country, methods, or interventions).
- Summarizing and assessing the overall quality of evidence on a particular topic.
Related terms
Section titled “Related terms”- Effect size