Test expected summary effects across study scenarios carefully. Explore heterogeneity, alpha, and required study counts. Build evidence plans with transparent assumptions and power estimates.
| Scenario | Model | Metric | Studies | Effect input | Estimated power | Studies needed |
|---|---|---|---|---|---|---|
| Planning SMD review | Fixed | SMD | 8 | 0.35 | 99.97% | 3 |
| Random effects OR review | Random | LOGOR | 12 | 0.7 | 97.51% | 7 |
| Generic effect update | Random | GENERIC | 10 | 0.22 | 96.34% | 8 |
The calculator estimates meta analytic power from an approximate pooled standard error. For fixed effects, pooled variance is v divided by k. For random effects, pooled variance is (v + tau²) divided by k.
Then pooled standard error equals the square root of pooled variance. The noncentrality value equals absolute expected effect divided by pooled standard error. Power comes from the normal distribution using the selected alpha and sidedness.
For standardized mean difference, within study variance is ((n1 + n0) / (n1n0)) + (d² / (2(n1 + n0 - 2))). For log odds ratio, the tool uses expected cell counts from the control event rate and expected odds ratio.
Required studies are found by increasing k until projected power reaches the chosen target. These are planning approximations. Real projects may need sensitivity checks and scenario analysis.
A meta analysis power calculator helps researchers test if a pooled effect is likely to reach statistical significance. It supports planning before registration and before data extraction. Good planning reduces weak syntheses, underpowered projects, and avoidable revisions. This page estimates power for fixed and random effects models using clear assumptions.
Power is the chance of detecting a real summary effect. Low power can hide important evidence. It also increases uncertainty in conclusions. Meta analyses can still be underpowered, even with several studies, when effects are small or heterogeneity is high.
This calculator estimates the standard error of the pooled effect and the related test statistic. It then converts that signal into statistical power. You can choose a generic inverse variance input, a standardized mean difference setup, or a log odds ratio setup. That makes the tool useful across medical, social, and behavioral research.
Start with the expected true effect. Then add the number of studies, alpha level, and sidedness. If you select the standardized mean difference option, enter average treatment and control group sizes. If you select log odds ratio, enter the baseline event rate and expected odds ratio. For random effects, include tau squared or I squared.
The calculator returns achieved power, pooled standard error, confidence interval, and noncentrality. It also estimates the required number of studies for your target power. A higher effect, more studies, or larger studies usually increase power. Greater heterogeneity usually lowers it.
Use the result during protocol writing, grant planning, and feasibility reviews. It is useful for evidence synthesis, living reviews, and update decisions. You can compare fixed and random assumptions to see how robust your plan is. That makes expectations more transparent before the review starts.
Power estimates depend on assumptions. They do not replace full methodological judgment. Still, they give a disciplined starting point for realistic meta analysis design. Sensitivity checks, subgroup plans, and effect metric choice should also be reviewed before relying on any single projected power estimate alone.
It estimates the projected power of a planned meta analysis. It also shows pooled uncertainty, implied heterogeneity, and the study count needed to reach a target power level.
Yes. Choose the random effects model and enter tau² directly, or enter I². The tool converts the heterogeneity input into the variance used in the planning calculation.
It supports generic inverse variance data, standardized mean difference planning, and log odds ratio planning from an expected odds ratio and baseline event rate.
No. The result is an approximation for study planning. Real evidence synthesis can differ because of unequal study sizes, missing data, effect mis specification, and model choice.
Power can remain low when the expected effect is small, average study precision is weak, or heterogeneity is large. These factors widen the pooled standard error.
Use I² when you want a familiar percent summary. Use tau² when you already have an estimated between study variance from earlier reviews or pilot evidence.
Yes. After calculation, you can download a CSV file or a PDF summary. That makes it easier to keep planning records with grant or protocol documents.
Use prior reviews, pilot data, expert elicitation, or a conservative clinically meaningful effect. Testing more than one scenario is usually the safest planning approach.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.