Revolution of the Clinical Data Analysts: How Innovative Trial Methodologies are Opening Up Career Opportunities

Clinical Researcher—June 2022 (Volume 36, Issue 3)


Marthe Masschelein


Clinical research is in the grips of a revolution that has caused a wave of new career opportunities.

The digitalization of trials and centralized statistical monitoring, for example, has generated the need for a new breed of data analysts—people with a broad range of skills and with a promise of significant job satisfaction.

This column focuses on the evolving role of the clinical data analyst, how such analysts contributed to the development of the Pfizer-BioNTech COVID-19 vaccine (Comirnaty®), and the future of clinical data analytics.

A Unique Skill Set

The industry has seen a shift in how clinical trials are designed and conducted in recent years.

Advances in computing power and data analytics have contributed to the birth of centralized statistical monitoring, which replaces traditional, retrospective source data verification with the near real-time analysis of data as they accumulate.

Assuming that data generated at each site should be roughly similar, analysts compare the collected information to identify site-level, country-level, and patient-level “outliers” for further investigation.

The review of anomalies in the data protects data quality by alerting sponsors to potential issues and enabling them to take corrective action before the problem can impact data integrity. In turn, this optimizes development pathways and shortens time to market access.

This enhanced way of conducting trials has given rise to a new breed of clinical data analysts.

Parts of a (Whole) Role

The goal of clinical data analysts is to identify anomalies in the data that could indicate a potential risk to the customer. So it’s all about data quality and integrity, and there are two main parts to the role.

The first is the system setup, which includes data consolidation. This involves harmonizing all the data from the customer’s various data sources, such as the electronic data capture and laboratory data, and feeding it into a risk-based quality management platform.

Second, a statistical engine is applied to all that data and the relevant results collected for review. Analysts will look for atypical data patterns, describe them in a “risk signal,” and present these signals to the customer’s study team. The analysts then support the team to decide whether the signals reveal a (potential) risk for data quality and integrity and whether they require continued monitoring or immediate follow-up (e.g., further investigation or corrective measures).

Probably the nicest part about the analyst job is that it takes a process from beginning to end—from uploading the data to presenting the results—rather than just covering a small part of the action.

This well-rounded process, however, is not for everyone, as the two parts of the job require different skill sets.

For the data harmonization and system setup, analysts need to do some SAS programming, although it isn’t very advanced, and data management and data manipulation. They need some technical aptitude and critical thinking skills—an analytical mind to review the data. They need good writing skills to describe the signals and good communication skills to present them.

The role may change as the organization scales and leaves its start-up roots behind. There may be opportunities for people to specialize in specific parts of the pathway, whether data harmonization, set-up, data review, or presenting the signals. This would allow people to play to their strengths while also honing their skills in other areas.

Rewarding Work

Analysts like the ones on the author’s team work in pairs for quality assurance and very closely with their customers, almost like an extension of the in-house study team. This can enrich professional relationships for both the analytics service provider and the sponsor.

Multiple clinical data analysts were, for example, part of a team from the author’s company that worked on the statistical analysis of data from Pfizer-BioNTech’s COVID-19 vaccine trials, helping to accelerate the development of this time-critical mRNA drug product. The safety and efficacy study was highly complex and recruited at a rate of 5,000 people a week. It combined Phases I, II, and III and included more than 43,000 people from 150 global sites, generating a massive volume of data.

The team supported Pfizer´s in-house analyst group by performing daily data analysis to ensure risk signals were identified, investigated, and mitigated in near-real-time and developed a suite of data visualizations to communicate potential risk areas. This allowed Pfizer to increase efficiencies so that it submitted drug applications in record time.

Analysts also work closely with other departments, such as the product, research, and commercial teams, making the role extremely varied. According to their assigned subject matter expert specialties, analysts further act as mentors to junior members of the team and customers. Having so much interaction with people in different clinical research functions and roles enables analysts to learn a great deal and build their expertise in new and vital areas.

Future of Clinical Data Analytics

Clinical research is evolving rapidly, making it something of a challenge to keep up but also presenting aspiring clinical data analysts with tremendous opportunities. Roles at companies specializing in centralized statistical monitoring are unique in that they embody a best-in-class approach to conducting clinical trials. These companies are always looking for people who are interested in and have a passion for the profession and for finding ways to make clinical trials better. Who doesn’t want to do that?

marthe masschelein

Marthe Masschelein is Vice President for Data Services with CluePoints.