Sample Size Calculator

Determines the minimum number of subjects for adequate study power

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Study Group Design vs. Two independent

study groups vs. One study group

vs. population Two study groups will each receive different treatments. One study cohort will be compared to a known value published in previous literature. Primary Endpoint Dichotomous

(yes/no) Continuous

(means) The primary endpoint is binomial - only two possible outcomes.

Eg, mortality (dead/not dead), pregnant (pregnant/not) The primary endpoint is an average.

Eg, blood pressure reduction (mmHg), weight loss (kg) Statistical Parameters Anticipated Means Group 1 ± Group 2 % Mean % Increase % Decrease Enrollment ratio Anticipated Incidence Group 1 % Group 2 % Incidence % Increase % Decrease Enrollment ratio Anticipated Incidence Known population % Study group % Incidence % Increase % Decrease Anticipated Mean Known population ± Study group % Mean % Increase % Decrease Type I/II Error Rate Alpha Power

Load an Example Press 'Calculate' to view calculation results.

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.

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.

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.

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.

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