Sample Size Calculator
Determines the minimum number of subjects for adequate study power
Study Group Design
Two study groups will each receive different treatments.
The primary endpoint is binomial - only two possible outcomes.
Eg, mortality (dead/not dead), pregnant (pregnant/not)
Dichotomous Endpoint, Two Independent Sample Study
|Group 1 ||690
|Group 2 ||690
|Incidence, group 1 ||35%|
|Incidence, group 2 ||28%|
About This Calculator
This calculator uses a number of different equations to determine the minimum number of subjects that need to be enrolled in a study in order to have sufficient statistical power to detect a treatment effect.1
Before a study is conducted, investigators need to determine how many subjects should be included. By enrolling too few subjects, a study may not have enough statistical power to detect a difference (type II error). Enrolling too many patients can be unnecessarily costly or time-consuming.
Generally speaking, statistical power is determined by the following variables:
- Baseline Incidence: If an outcome occurs infrequently, many more patients are needed in order to detect a difference.
- Population Variance: The higher the variance (standard deviation), the more patients are needed to demonstrate a difference.
- Treatment Effect Size: If the difference between two treatments is small, more patients will be required to detect a difference.
- Alpha: The probability of a type-I error -- finding a difference when a difference does not exist. Most medical literature uses an alpha cut-off of 5% (0.05) -- indicating a 5% chance that a significant difference is actually due to chance and is not a true difference.
- Beta: The probability of a type-II error -- not detecting a difference when one actually exists. Beta is directly related to study power (Power = 1 - β). Most medical literature uses a beta cut-off of 20% (0.2) -- indicating a 20% chance that a significant difference is missed.
Post-Hoc Power Analysis
To calculate the post-hoc statistical power of an existing trial, please visit the post-hoc power analysis calculator.
References and Additional Reading
- Rosner B. Fundamentals of Biostatistics. 7th ed. Boston, MA: Brooks/Cole; 2011.