How Prevalence Data Can Misguide You

When my team and I set out to simplify the way feasibility and biz dev teams run RFPs and plan their clinical trials we had numerous conversations with medical directors and feasibility experts to make sure we understand their processes and align with their KPIs. Our goal was to create a tool that positions them better in front of their managers and clients. Their insights together with our experience helped me come to a new level of understanding of what stops us from being better at clinical trials.

This article is about revealing the King of running feasibility for a new clinical trial – Prevalence Data. Whether this is a feasibility professional from IQVIA or Syneos or an expert from a local CRO, or a niche biotech company, they all agree that one of the first things they check is the epidemiological data for the condition. Just yesterday I had a chat with the COO of an innovative biotech company who inspired me to share today things that not everyone will tell you:

1. Prevalence Data is great as a starting point.

Knowing how many patients there are in each country is very important. Yet, this is far from knowing how many patients will be eligible for your clinical trial protocol or knowing how many patients will be interested in joining your trial (this is where you might benefit from a close relationship with patient advocates and local practitioners).

This is why the most experienced feasibility experts define prevalence data as a starting point to get the big picture of epidemiology. Not having it at first glance can lead to opening a new region where patients with the indication are few. A good example is all the ulcerative colitis trials in South Europe, where competition is rising along with the number of sites recruiting 0 patients.

2. Country selection based on Prevalence Data is really challenging.

It’s a fact that for most indications accurate prevalence data is hard to find, especially if you look for information per country. Most experts rely on publications and condition-specific institutions to get data for each indication and region. This makes collecting data time-consuming and not very reliable because different sources are hard to compare. Hard comparison means hard country selection.

There are some databases (including TrialHub) that have managed to identify accurate and reliable prevalence data for thousands of diseases. Yet, we need very strong patient registers or standardized EMRs in order to fix this problem completely.

3. Sometimes Incidence is more important than Prevalence.

Prevalence is calculated as the number of patients per 100 000 people who have the condition. Incidence predicts the annual rate of patients who are expected to get diagnosed with the condition. For some protocols, you need patients that are newly diagnosed which would mean that prevalence players a smaller role than incidence. You should be careful then not to look at the wrong place for patients (for example they are still with their GPs and not with the specialists).

4. Prevalence Data does not mean a successful Patient Recruitment Plan.

The COO I had a chat with yesterday, told me something she had heard from ex-colleagues:

“When you know where the fish is, this is where you go fishing”

Though patients are not fish, this was very informative. Our industry is known for not being very patient-focused. However, things are changing and I see more and more professionals trying to think of the patient first. This is not always the case when planning a clinical trial though.

Numbers are all that matters: how many patients are there, how many patients are eligible, how many are at the sites? Numbers, though, don’t tell you about what challenges patients have in order to consider clinical trials. To them clinical trials are an alternative to their treatment, so paying attention to the standard of care in the country might give you a lot more answers than prevalence data.

Final Words

The COO also shared what she believes is a better strategy and I fully agree with her:

“Get prevalence as a starting point to know where to go and then identify the most engaged and experienced sites – this is what will bring you to success”

I believe there is no one formula for success when planning a clinical trial. You should be as flexible as possible with the different combinations of feasibility criteria in order to identify challenges and see the opportunities ahead.

First published on LinkedIn

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