Lost to Follow-up
Subjects whose outcome data are missing because they did not complete planned visits.
Definition
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.What this means in practice
Long-term implant studies and digital therapeutic trials are particularly prone to LTFU; remote follow-up models can mitigate but introduce data-quality questions.Examples
- A sponsor of a long-term orthopedic implant study develops a detailed patient retention plan, including regular communication, travel assistance, and flexible follow-up options, to minimize lost to follow-up over a 10-year period.
- During a clinical trial for a novel digital therapeutic, the sponsor conducts an interim analysis and discovers a higher-than-expected lost to follow-up rate, prompting an immediate review and adjustment of patient engagement strategies.
- A post-market clinical follow-up (PMCF) study for a cardiovascular device accounts for potential lost to follow-up by over-enrolling participants, ensuring sufficient data remain for robust analysis even with anticipated attrition.
- •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.
Frequently asked questions
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· Same categoryTrial design that pre-specifies opportunities to modify aspects (sample size, arms, endpoints) based on interim data.
Any untoward medical occurrence in a subject, whether or not related to the device.
Data integrity principles: Attributable, Legible, Contemporaneous, Original, Accurate (plus Complete, Consistent, Enduring, Available).
An IVD's ability to detect small quantities of an analyte.
Where this term appears across MedTech Terms.
Sources
3 sourcesSources support the definition or relevant regulatory context, not necessarily every claim on this page. Each is graded against our source-tier hierarchy so you can see what rests on binding law versus commentary.
- 1ICH E9(R1)Tier 2 VerifiedICHich.org
- 2ICH GuidelinesTier 2 VerifiedICHich.org
- 3ISO 14155 Standard PageTier 1 VerifiedISOiso.org
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