Loss to follow-up reduces power, biases estimates if non-random, and is a leading reason for clinical-trial credibility concerns. Sponsors should pre-specify thresholds, sensitivity analyses, and retention strategies. Missing data handling should be planned in the statistical analysis plan, using approaches such as multiple imputation and tipping-point analyses that show how results would change under different assumptions. Retention strategies include flexible visits, remote follow-up and reminders. For long-term implant studies, registries and linked databases can help track outcomes for subjects who stop attending visits.

Why it matters

Long-term implant studies and digital therapeutic trials are particularly prone to LTFU; remote follow-up models can mitigate but introduce data-quality questions.

Common pitfalls

•Failing to document and justify the chosen LTFU thresholds can lead to regulatory scrutiny.
•Assuming LTFU is always missing completely at random (MCAR) without conducting appropriate sensitivity analyses is a common statistical error.
•Not having a pre-specified plan for handling missing data due to LTFU can result in biased study conclusions.
•Overlooking the impact of LTFU on the statistical power of the study can lead to underpowered trials.
•Neglecting to implement specific retention strategies tailored to the study population and device type increases the risk of high LTFU rates.

Sources

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Why compare MedTech terms side by side?

MedTech terminology is full of pairs that look interchangeable but carry very different regulatory, clinical, and commercial consequences. Picking the wrong framework, pathway, or standard early in a project can add months to a submission, invalidate clinical evidence, or trigger an audit finding. Side-by-side comparison is the fastest way to surface those differences before they become costly mistakes.

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Common comparison patterns

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.
EU frameworks - MDR vs IVDR, CE Mark vs UKCA.
Cybersecurity artifacts - SPDX vs CycloneDX SBOM, CVE vs CVSS.
Post-market actions - Recall vs field action, Removal vs correction.

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MedTech Terms is a vendor-neutral community resource sponsored by Blue Goat Cyber. Definitions are written for educational use and are not legal or regulatory advice.