Pharmaceutical Outsourcing Q3 2023 - 15

CLINICAL TRIALS
SECTION TITLE
based data cleaning focusing on primary endpoint and key study
datapoints. Currently, I see a lack of understanding about what key, or
critical, data is. When discussing critical data with clients I've been told
several times that every field on the adverse event (AE) page is critical
data because the study is evaluating safety of the drug, but this is not
true. If, for example, their primary endpoint for safety evaluation is the
frequency of treatment emergent AEs, a sporadic extra field added to
the adverse event page of " was this event caused by COVID-19? " is not
critical data. What is critical to know is the date the event occurred,
and the date study drug was administered, because without that
you cannot establish if the event is treatment emergent and should
be included in the analysis. Knowing what the critical data is allows
us to focus our data cleaning efforts and without building a clear
understanding of critical data we cannot move to a more risk-based
review led by data scientists.
Once we know what the critical datapoints are in a study, the next
question is where to focus the rest of our data cleaning efforts.
Data visualizations produced by data scientists provide us with this
understanding. They give direction to the data management teams
on where else there may be data issues rather than continue with
the outdated method of cleaning everything. But again, it is about
prioritizing and using a risk-based approach. Using a data scientist is
a bit like using a metal detector to help you locate where to dig to find
treasure in a field. You don't have time to scan the whole field, so you
must decide which part of the field to scan. Data visualizations take
time to program, and we don't have the time or the budget to check
the whole database so the study team must think very carefully about
what data is important or where we anticipate errors in the data.
Preparing For and Predicting
Future Trends
In the future, as budgets and timelines are squeezed, we are going to
need to do more risk-based cleaning, supported by data scientists. If
we accept every data point does not need to be perfectly clean, data
management teams will spend less time cleaning and sites will spend
less time answering queries, which in turn will lead to cutting costs,
and hopefully improving the quality of our critical data points.
Extrapolating the principles of risk-based monitoring and applying
them to risk-based cleaning could allow a real growth in data science
in the years to come.
However, if current trends continue, we will get more and more data in
our clinical trials. If we want to reduce the costs of drug development,
we also need to tackle this increasing volume of clinical trial data.
Every data point that is collected must be entered into a database,
cleaned, and stored and this comes at a cost. We need to take a step
back and carefully consider what data we are collecting in our clinical
trials and why.
For example, if a sponsor wants to test whether patients prefer writing
in a paper or electronic diary, they are inclined to call it a clinical trial
because it involves patients. From a data management perspective,
the data then gets entered into a fully validated system, is cleaned
to our high-quality standard, and stored in a secure facility. But they
are not diagnosing or treating anything and are not providing an
intervention to the patients care, they are just asking for preference,
so does this need this gold standard approach to this data? The
drinks industry does this type of preference study all the time - 'do
you prefer elderflower or orange?'. The pharmaceutical industry
needs to define when an investigation is a clinical trial and when it
is not to allow more opportunities for lower cost data collection and
storage. We need to understand more about whether data needs to
be collected to the highest standard, as seen in our clinical trials, or if
it is only to add insight.
Conclusion
The increasing complexity and scale of clinical trials is leading to
increased demand for specialist data-focused Contract Research
Organizations (CROs). But there is also a need to increase genuine
collaboration between experts and sponsors. Pharma companies
are starting to recognize the value of data management and data
scientists, but we need to be involved in every stage - right from
trial design.
As we see more big data and growing data sources it will be
increasingly important not to collect data for data's sake. Data
managers can help revolutionize how we collect and store data,
while data scientists inform a risk-based approach to cleaning and
prioritization. Both will ultimately save sponsors money.
Effecting this change is likely to be a slow creep. But by combining
new technology with the expertise of data science teams we can
ensure the industry is prepared for future trends and can optimize its
decision making.
References
1. https://www.globenewswire.com/news-release/2021/01/12/2157143/0/en/RisingProtocol-Design-Complexity-Is-Driving-Rapid-Growth-in-Clinical-Trial-Data-VolumeAccording-to-Tufts-Center-for-the-Study-of-Drug-Development.html
Nicola
Griffiths, Manager of Clinical Data Operations at Phastar,
has been working in the industry for over twenty years. Nicola
started her career in data management at CRO Nottingham
Clinical Research, (later bought by Worldwide Clinical Trials),
working as a data entry assistant on paper CRF studies before
moving into project management for several years. Nicola then joined Reckitt
Benckiser (a consumer healthcare company) where she worked across various
roles before returning to Data Management. At Reckitt Benckiser, Nicola worked
on various clinical trials, not just on medicines but also on medical devices,
cosmetics, and even general products. Nicola moved into management at SQN
Clinical, (later to become Veristat), in the data management department, before
moving to her current role at Phastar.
pharmoutsourcing.com | 15 | July/August/September 2023

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Pharmaceutical Outsourcing Q3 2023

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