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Clinical & Trials

Sample Size and Statistical Power

In one line

Pre-trial calculation of the number of subjects needed to detect a specified treatment effect with acceptable Type I and Type II error rates.

Definition

Sample size and power calculations determine how many subjects a clinical trial must enroll to detect a pre-specified effect size with acceptable false-positive (Type I error, alpha, typically 0.05 two-sided) and false-negative (Type II error, beta, typically 0.20 — i.e., 80% power) rates. Inputs include the expected effect size, the variance of the outcome, the test statistic, allocation ratio, and any planned interim analyses. Under-powered trials are a leading cause of inconclusive pivotal studies.

Why it matters

MedTech pivotal trials commonly target 80-90% power for the primary endpoint. Adaptive designs and Bayesian designs allow for sample-size re-estimation under pre-specified rules.

Common pitfalls

•Powering only the primary endpoint and finding the trial cannot answer key secondary endpoints used for label or HEOR.
•Optimistic effect-size assumptions that produce a too-small sample size.

Sources

ICH E9 Statistical Principles · ICH
FDA Guidance — Adaptive Designs for Clinical Trials (2019) · FDA
ClinicalTrials.gov · NIH

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Regulatory pathway choice - 510(k) vs PMA, 510(k) vs De Novo, Abbreviated vs Special 510(k).
Quality systems - QSR vs ISO 13485, QMSR vs QSR.
Software classification - SaMD vs SiMD, Locked vs adaptive AI.
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Cybersecurity artifacts - SPDX vs CycloneDX SBOM, CVE vs CVSS.
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