MedTech World Magazine: Clinical trial regret

In MedTech, innovation naturally gets most of the attention. New technologies, novel delivery mechanisms, and breakthrough concepts dominate conversations. But when it comes to clinical trials, it is the data — not the device — that ultimately determines whether a product progresses, stalls, or is forced back to the drawing board.

Christine Nimalasiri, Clinical Director, Ascend CRO, breaks down why clinical trial regret is more common than many sponsors expect — and how stronger data strategy can prevent costly repetition.

At Ascend CRO, almost a quarter of our work last year involved helping sponsors repeat their First-in-Human (FIH) clinical trial. Ouch. We work with MedTech teams across all stages of development, from early feasibility through to post-market studies. While the technologies themselves vary widely, the data challenges we see are remarkably consistent. Too often, promising devices run into trouble not because they lack potential, but because the data generated fails to meet regulatory expectations.

The good news is that this kind of clinical trial regret is avoidable when you choose the right partners.

Below are the five most common data pitfalls we encounter in clinical trials — and how sponsors can avoid the costly mistake of having to do it all again.

Not Starting With the End in Mind

One of the most frequent mistakes is designing a trial around what is interesting, familiar, or operationally convenient, rather than what regulators actually need to see.

This approach can produce data that looks robust on the surface but does not adequately support safety or performance claims. Without clear alignment to a regulatory pathway, sponsors may complete a study only to discover it cannot be used in a key market submission.

A stronger approach is to define the regulatory objective first. This means clarifying the intended use, target claims, and jurisdictional pathway before the protocol is finalised. When the end goal is clear, the data requirements become far more focused, efficient, and defensible.

Collecting Too Much Data

In an effort to reduce risk, many trials collect far more data than is actually required. While this may feel conservative, it often creates new problems.

 Large datasets increase the burden on investigators and sites, raise the likelihood of missing or inconsistent data, and complicate analysis. They also increase the level of scrutiny during monitoring and audits.  

Regulators are not impressed by volume. They are looking for relevance, consistency, and clarity. A lean dataset that clearly supports defined endpoints is almost always more persuasive than an expansive one with gaps and inconsistencies.

Poor Pre-Clinical Design

Poor pre-clinical data is one of the highest hidden risks going into a First-in-Human medical device trial. It doesn’t just weaken the science; it undermines the entire clinical, regulatory, ethical, and operational foundation of the study. 

FIH trials are approved on trust in the pre-clinical package. Weak or poorly designed data breaks that trust immediately. When gaps emerge, sponsors are often forced into protocol amendments, trial pauses, or additional non-clinical testing.  

Each of these outcomes extends timelines, increases burn rate, and erodes confidence. FIH rework is among the most expensive fixes in medical device development. 

Treating Data Quality as a Final Step

Another common pitfall is treating data quality as something to address at the end of the trial. By that point, options to correct issues are limited.  

Regulators expect data quality to be actively managed throughout the study, not retrospectively cleaned. Ongoing data review allows teams to identify trends, inconsistencies, or protocol deviations early, when corrective action is still possible.  

Proactive quality oversight protects both timelines and credibility, and significantly reduces the risk of data being questioned later.  

Overlooking Traceability

Traceability is one of the most critical and most underestimated aspects of clinical trial data. 

Regulators need to clearly understand how reported results link back to source data. Without strong documentation, validated systems, and reliable audit trails, even accurate data can be challenged.  

Traceability is not administrative overhead. It is how trust in the data is established and maintained, particularly during audits and regulatory review. 

The Takeaway

For sponsors, avoiding these pitfalls is not just about compliance — it is about protecting time, capital, and credibility.

Ascend’s role is to help sponsors make the right decisions early, before they become expensive or irreversible. We work closely with sponsors to translate regulatory expectations into practical trial design, ensuring data is not only collected correctly, but is usable across the full development pathway. This includes stress-testing protocols, simplifying data strategies, and building quality and traceability into trials from day one.

The result is confidence. Confidence that the data will stand up to scrutiny, support future submissions, and reduce the need for rework. Because nothing defines clinical trial regret quite like having to run your First-in-Human study for a second time.

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