Pharmaceutical Outsourcing Q3 2023 - 14

CLINICAL TRIALS
Visualizing for the Future -
Prioritizing Data and Increasing
Expertise for Enhanced Data
Science Decision-Making
Nicola Griffiths
Manager of Clinical Data Operations
Phastar
Data science is the current buzzword in the pharmaceutical industry.
It offers solutions to the challenges of increasing clinical data sources
in a multi-provider, decentralized, digitally-enabled environment. But
to make sure it is more than just a buzzword we need to recognize
data science as a distinct discipline and indispensable component to
modern-day clinical trials.
Traditionally, data managers were used to Case Report Form (CRF)
data. It was our gold standard, it was what we cleaned, what we
understood. There were methods in place for collecting it and it was
very regimented. But the data landscape is changing. In 2022, Phase
III clinical trials were generating an average of 3.6 million data points
- three times more than a decade previously.1
data was also coming from more sources without the rigorous checks
teams were used to.
In this messy data environment, data scientists provide fundamental
support to traditional data management teams, helping streamline
cleaning and identifying potential issues which previously might
have gone undetected. For example, what are the data points the site
is making lots of changes to? Does that mean they are not certain
about that data? Is there a quality issue?
Data scientists allow us to examine the consistency of data, prioritize
what is important and separate out what is not. This allows us to take
a more risk-based approach to data cleaning, ultimately saving time
and resources.
Recent Trends in Clinical Trials and
Changes to Data Management
Data management teams have been buried in data in recent years.
This is not just remote monitoring data but also lab data, gene
data, scans, personal monitoring devices - almost every source
imaginable. We are receiving multiple data sources, from multiple
vendors, all managed in a different way prior to being collated in Data
Management departments and have the challenge of ensuring the
final quality of that data, without any ability to impact the collection
methods or data sources.
This huge volume of
While data scientists can undoubtedly help prioritize this data, we
also need to educate those providing data about the need to follow
good clinical practice (GCP) i.e., that the information should be
recorded, handled, and stored in a way that allows accurate reporting,
interpretation, and verification, as data destined for a clinical trial has
to be controlled to a very high standard. We need to go back to the
source, whether that is labs or third-party suppliers, and have the
conversations we had in data management 20 or 30 years ago. How
do you control the quality and integrity of your data? Who recorded
the original data? Who inputted the data into your database? How
do you verify the inputted data versus the source? Have you got an
audit trail to track any changes made to that data? How do we know
who has changed the data and why? There is little point in the Data
Management team having tight control over the data once it reaches
us if there isn't any quality control of the original data.
We also need to make sure we are selecting the most effective form
of data for trial design. Often, data managers do not get a seat at
the table when it comes to trial design. This can lead to data being
collected for the sake of medical interest and sponsors paying for
peripheral data which has no impact on, for example, regulatory
approval of their drug. Involving data managers in trial design can
cut this cost.
This increase in the volume of data alongside the current economic
climate tightening the clinical budgets highlights the need for riskPharmaceutical
Outsourcing | 14 | July/August/September 2023
Pharmaceutical Outsourcing Q3 2023

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