Meta Regression

TL;DR

• Aggregates results from prior studies to detect study-level factors that influence outcomes.
• Helps explain inconsistencies or variations across study results.
• Provides actionable insights for research design, clinical decisions, or policy.

Definition

Meta regression is a statistical technique used to analyze the results of previous research studies in order to identify factors that may have influenced the results.

Explanation

Meta regression examines variation in effect estimates across studies by relating those estimates to study-level characteristics (moderators). It is used to explore reasons behind inconsistencies or variations in study results and to provide insights that can guide future research or decision-making.

Examples

Drug effectiveness for depression

A meta regression analysis of studies investigating the effectiveness of a particular drug for treating depression may find that the drug’s effectiveness varies depending on the severity of the depression, with more severe cases showing greater improvement. This information can be used to better tailor treatment plans and improve outcomes for patients.

Government spending and economic growth

A meta regression analysis of studies examining the relationship between government spending and economic growth may find that the impact of government spending on economic growth varies depending on the type of spending and the stage of the economic cycle. This information can be used to inform policy decisions and improve economic outcomes.

Use cases

• Understanding reasons for inconsistencies or variations in study results.
• Informing and tailoring treatment plans based on study-level findings.
• Guiding policy decisions by clarifying how effects vary with contextual factors.
• Providing insights for future research design and priorities.