What is the most common reason a pivotal MedTech trial ends up underpowered?

Optimistic assumptions about the expected effect size, often based on small early feasibility data or investigator expectation rather than conservative, well-justified estimates, are a leading cause of trials that enroll too few subjects to reliably detect a true treatment effect, resulting in an inconclusive result even when a real clinical benefit exists. Regulatory reviewers scrutinize the justification behind the assumed effect size specifically because of how often this assumption drives underpowering.

Why is powering only the primary endpoint sometimes not enough?

A trial sized purely to detect the primary endpoint's effect may lack sufficient statistical power to draw reliable conclusions about secondary endpoints that matter for labeling claims or for the health economics and outcomes research case needed for payer coverage, forcing sponsors into costly additional studies after pivotal completion. Sample size planning increasingly needs to account for which secondary endpoints will be used to support broader label or reimbursement claims, not the primary endpoint alone.

How do adaptive and Bayesian trial designs change the sample size planning process?

Adaptive and Bayesian designs, addressed in FDA's 2019 guidance on adaptive designs for clinical trials, allow for sample-size re-estimation partway through the trial based on interim data, using pre-specified rules agreed with FDA in advance, which can reduce the risk of enrolling either too many or too few subjects compared to a fixed sample size fully committed at the outset. These designs require substantially more upfront statistical planning and regulatory discussion than a traditional fixed-sample trial, since the adaptation rules themselves must be validated and agreed before enrollment begins.

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Clinical & TrialsClinical Evidence

Sample Size and Statistical Power

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

Reviewed by Christian Espinosa, Founder, Blue Goat CyberLast reviewed May 9, 2026

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.

What the regulation says

FDA Guidance: 'Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests' and ICH E9 'Statistical Principles for Clinical Trials' both require pre-specified sample-size justification in the protocol.

What this means in practice

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.

Examples

To evaluate a new surgical robot's ability to reduce recovery time, researchers calculated that 250 patients per arm were needed to detect a 1-day reduction with 80% power and a 5% Type I error rate.
A MedTech company developing a novel implantable sensor performs a sample size calculation to ensure its pivotal trial can detect a statistically significant improvement in a specific physiological marker compared to standard care.
During the design of a clinical trial for a new therapeutic device, an adaptive design allowed for sample size re-estimation after an interim analysis, adjusting the recruitment target while maintaining statistical power and controlling for Type I error.

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.

Frequently asked questions

The calculation requires the expected effect size for the primary endpoint, the variance of that outcome measure, the chosen statistical test and allocation ratio between study arms, the acceptable Type I error rate (alpha, conventionally 0.05 two-sided), and the acceptable Type II error rate (beta, conventionally 0.20, corresponding to 80 percent power), all of which must be pre-specified in the protocol per ICH E9. Any planned interim analyses must also be accounted for in the calculation, since they affect the overall error rate spent across the trial.

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Adaptive Trial Design

Trial design that pre-specifies opportunities to modify aspects (sample size, arms, endpoints) based on interim data.

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Bayesian Trial Design

Trial design that uses Bayesian statistics, often incorporating prior data from related studies or registries.

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ICH E6 Good Clinical Practice(GCP)

International standard for the design, conduct, and reporting of clinical trials.

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Pivotal Trial

Definitive clinical study designed to establish safety and effectiveness for marketing authorization.

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Tier 2Regulator guidance and consensus· 2Tier 3Peer-reviewed and registry evidence· 1

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

·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.
·Inputs include the expected effect size, the variance of the outcome, the test statistic, allocation ratio, and any planned interim analyses.

Remember this

Watch out: Powering only the primary endpoint and finding the trial cannot answer key secondary endpoints used for label or HEOR.

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