A Continuous Outcome Superiority Trial is a clinical trial method that aims to show that a treatment is superior to another treatment in terms of a continuous outcome. Continuous outcomes are numerical values, such as blood pressure, cholesterol levels or pain intensity, which can be measured on a scale.
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Goal and concept
The main objective of a Continuous Outcome Superiority Trial is to demonstrate that the new treatment offers a significant advantage over the existing treatment. In contrast to equivalence or non-inferiority trials, a measurable and clinically relevant difference that proves the superiority of the new treatment is actively investigated.
Free online calculator to calculate the sample size
Use our online calculator to easily and quickly calculate the required sample size for your superiority study with continuous results.
Free online calculator to calculate the sample size
The calculation of the sample size plays a crucial role in ensuring that the study has sufficient statistical power to detect a difference. This takes into account the expected difference between the groups, the standard deviation of the results and statistical parameters such as significance level and power. Our online calculator will help you to precisely determine the optimal sample size.
Application and requirements
A typical Continuous Outcome Superiority Trial compares two antihypertensive drugs by measuring the mean reduction in systolic blood pressure. If the new treatment reduces blood pressure by an average of 5 mmHg more than the existing therapy, this indicates statistical and clinical superiority. To prove this, a clear hypothesis, a precise definition of the endpoint and detailed statistical planning are required. Such studies ensure valid and meaningful results.
Notes on sample size calculation for superiority studies with continuous outcomes
The calculation of the sample size is based on the expected difference between the groups, the standard deviation of the results, the significance level and the statistical power. These parameters ensure that the study is robust enough to test the hypothesis of superiority.
Significance level (alpha)
The significance level (α) defines the risk of an error of the first kind, i.e. the probability of finding a difference even though none exists. An alpha of 0.05 is usually used, which corresponds to a 5% risk .
Power (1-Beta)
The power (1-β) indicates how likely it is that an actual difference will be detected between the groups. A typical power of 80% to 90% ensures that the study is sufficiently sensitive to identify a clinically meaningful difference.
Standard deviation (σ)
The standard deviation (σσ) indicates the variability of the results within each group. It significantly influences the sample size: the greater the variability of the results, the larger the sample must be in order to detect a difference.
Expected difference (Δ)
The expected difference (ΔΔ) defines the minimum clinically relevant difference between the groups. For example, a difference of 5 mmHg in blood pressure could be considered relevant.
Formula for calculation
The sample size is calculated using the following formula:
n=2⋅f(α,β)⋅σ2Δ2n = \frac{2 \cdot f(\alpha, \beta) \cdot \sigma^2}{\Delta^2}
Here, f(α,β)f(\alpha, \beta) stands for the Z-values determined by the significance level and the power, σ\sigma for the standard deviation of the results, and Δ\Delta for the expected difference. This formula ensures that the sample size is sufficient to obtain reliable and meaningful results in your superiority study.