Sample Size Calculation in Clinical Research

An adequately sized study is an ethical requirement: too few participants and a real effect may be missed; too many and participants are exposed to research without need.

What you need before you start

• The primary outcome and its type (proportion, mean, time-to-event).
• The study design — cross-sectional, comparative, diagnostic, etc.
• An expected value from published literature or a pilot study (prevalence, mean and SD, or expected difference).
• The significance level (usually α = 0.05) and power (usually 80% or 90%).

Estimating a single proportion

For a prevalence study, a common formula is n = Z² × p × (1 − p) / d², where Z = 1.96 for 95% confidence, p is the expected prevalence and d is the absolute precision. With p = 0.30 and d = 0.05, n ≈ 323.

Comparing two groups

For comparing means or proportions between two groups, the calculation depends on the expected difference, variability, α and power. Software such as G*Power, OpenEpi, or R packages (pwr) is typically used, and the parameters should be reported.

Allow for dropouts

If you expect 10% loss to follow-up, divide the calculated n by 0.9. State this adjustment explicitly.

Report it transparently

Your protocol should state the formula or software, every input value, the source of those values, and the final number. Examiners and ethics committees look for justification, not just a number.