Sample Size Calculator Clinical Trial

Sample Size Formula for Superiority Trial:

\[ n = 2 \times \left( \frac{Z \times \sigma}{\delta} \right)^2 \]

Sample Size Per Arm (n):

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1. What is Sample Size Calculation for Clinical Trials?

Sample size calculation is a critical step in clinical trial design that determines the number of participants needed to detect a statistically significant difference between treatment groups. Proper sample size ensures the trial has adequate power to answer the research question.

2. How Does the Calculator Work?

The calculator uses the sample size formula for superiority trials:

\[ n = 2 \times \left( \frac{Z \times \sigma}{\delta} \right)^2 \]

Where:

\( n \) — Sample size per arm (count)
\( Z \) — Z-value (unitless)
\( \sigma \) — Standard deviation (units)
\( \delta \) — Detectable difference (units)

Explanation: This formula calculates the required sample size per treatment arm for a two-arm superiority trial with continuous outcomes, assuming equal variance and normal distribution.

3. Importance of Sample Size Calculation

Details: Adequate sample size ensures sufficient statistical power to detect clinically meaningful differences while minimizing the risk of false positives (Type I errors) and false negatives (Type II errors).

4. Using the Calculator

Tips: Enter the Z-value (typically 1.96 for 95% confidence), standard deviation of the outcome measure, and the minimum detectable difference considered clinically important. All values must be positive numbers.

5. Frequently Asked Questions (FAQ)

Q1: What is the typical Z-value used?
A: For a two-sided test with 95% confidence level, Z = 1.96 is commonly used. For 90% confidence, Z = 1.645.

Q2: How do I determine the standard deviation?
A: Use data from previous studies, pilot studies, or published literature on similar populations and outcomes.

Q3: What is a clinically meaningful detectable difference?
A: This should be based on clinical expertise and represents the smallest difference that would change clinical practice.

Q4: Does this account for dropouts?
A: No, you should inflate the calculated sample size by the expected dropout rate (e.g., add 10-20%).

Q5: When is this formula appropriate?
A: For continuous outcomes in superiority trials with two parallel groups and normally distributed data with equal variances.