Estimate noninferiority sample size, power, and margin settings fast. Plan binary or continuous designs with balanced assumptions. Export outputs for transparent protocol decisions and team reviews.
| Scenario | Endpoint | Control | Treatment | Margin | Alpha | Power |
|---|---|---|---|---|---|---|
| A | Continuous | Mean 10.0, SD 2.5 | Mean 10.1, SD 2.5 | 0.20 | 0.025 | 0.80 |
| B | Continuous | Mean 22.0, SD 4.0 | Mean 22.1, SD 3.8 | 0.50 | 0.025 | 0.90 |
| C | Binary | Rate 0.65 | Rate 0.66 | 0.08 | 0.025 | 0.80 |
| D | Binary | Rate 0.78 | Rate 0.77 | 0.10 | 0.025 | 0.90 |
For continuous outcomes, the tool uses an approximate two-group noninferiority design: ncontrol = ((Z1-α + Zpower)² × (SDc² + SDt² / r)) / (M + (μt - μc))²
Here, r is the treatment-to-control allocation ratio. M is the positive noninferiority margin. The assumed difference is treatment mean minus control mean.
For binary outcomes, the tool uses: ncontrol = ((Z1-α + Zpower)² × (pc(1-pc) + pt(1-pt) / r)) / (M + (pt - pc))²
The adjusted sample size equals raw sample size divided by one minus dropout rate.
A noninferiority design helps researchers show that a new treatment is not unacceptably worse than a control. This method is common in clinical trials, biostatistics, and regulated study planning. The design depends on the endpoint, margin, alpha, power, and expected variability.
This noninferiority design tool estimates sample size for two-arm studies. It supports continuous outcomes and binary outcomes. You can set a one-sided alpha, define the target power, and apply an allocation ratio. The calculator also adjusts final enrollment for dropout.
Strong assumptions can shrink or expand the required sample. A tighter margin usually increases enrollment. Higher power also raises sample size. Larger standard deviations or more uncertain event rates can make the design less efficient. Balanced assumptions usually improve interpretability.
Use this tool during protocol drafting, feasibility review, grant planning, and sensitivity checks. It is useful when teams need quick design estimates before formal statistical programming. The export options help analysts share transparent planning outputs with investigators, reviewers, and operations teams.
This calculator provides planning estimates, not a full statistical analysis plan. Final trial design should consider endpoint definition, covariance structure, missing data, interim rules, and regulatory guidance. Even so, this page offers a fast and practical starting point for noninferiority study design.
A noninferiority margin is the largest acceptable loss versus control. It defines how much worse the treatment may be while still remaining clinically acceptable.
Noninferiority testing usually focuses on one direction. The main question is whether the treatment is worse than control by more than the allowed margin.
Yes. Enter a treatment-to-control ratio above or below one. Unequal allocation changes the variance term and the required group sizes.
A very small margin often increases the required sample size sharply. It can also make the study harder to complete within time and budget.
Yes. It supports continuous outcomes with means and standard deviations, and binary outcomes with expected event rates in both groups.
Dropout adjustment protects the effective analyzable sample. Without it, the final enrolled population may be too small for the target power.
No. It is a planning aid. Final protocols should also review estimands, missing data, stratification, analysis sets, and regulatory expectations.
Yes. The page offers CSV and PDF download options after calculation. That makes it easier to share planning outputs with teams.
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.