FSP Archives

The Use of AI in Clinical Trial Data Management

Clinical data managers play a key role in clinical trials, ensuring the integrity of the clinical data and bearing ultimate responsibility for preparing the data for statistical analysis. As clinical studies evolve, data management is becoming more complex with the use of multiple data sources, as well as the increased volume of data through case report forms, patient reported outcomes, laboratory data, electronic health records, imaging reports, and more.

Here, we discuss various ways artificial intelligence has the potential to accelerate clinical trial data management as well as some of the benefits and challenges of using these groundbreaking tools.

Transforming data cleaning in clinical trials

The traditional method of data cleaning involves manual checks and review of data listings. Data that falls outside the expected results is queried, which leads to time-consuming communications that are often prone to error. Additionally, a significant amount of time is spent programming and validating data checks and listings.

AI has the potential to transform data cleaning: AI tools can quickly spot outliers, inconsistencies, and errors in datasets that may be missed with traditional methods. An example of this is an exception listing, which compares elevated laboratory parameters with adverse events. This listing would seek to strike a correlation between laboratory values and concurrent adverse events. AI can also detect possible missing or duplicate data. Ultimately, AI can lead to faster data availability by improving clinical trial data analysis and cleaning.

Furthermore, AI can be used to detect fraudulent data. As data managers review one patient data at a time, AI tools can look at data at a site collectively and look for potential fraudulent data (for example, dosing for all patients at the site is on the same day and time).

Improving database development in clinical trials

The use of AI is becoming more prevalent in the software industry: electronic data capture (EDC) companies are using AI technology to translate plain text for edit check requirements (e.g., temperature should be within 36–40 Celsius) into the programmed edit checks. This will significantly improve the timelines for database development within the clinical trial space.

Shortening database lock timelines

AI can also shorten database lock timelines. Data managers are often tasked with manually reviewing data to ensure data accuracy as well as verifying whether there is missing data. An example of this is considering a study participant with colon cancer, but there is no prior therapy within the timeframe of the condition. Data managers require a significant amount of time to perform such reviews. AI can examine the data holistically, flag possible discrepancies, and reduce manual effort and human error, allowing for a cleaner database at a much faster rate.

The need for human involvement

While AI can process large amounts of data, identify suspicious patterns, and analyze images, human involvement is required to validate the AI functionality. Humans will also be required to perform ongoing reviews of the output and adjust the AI tools as the amount of clinical trial data increases. With the use of AI, individuals involved in the clinical trial can focus their skills and time on evaluating complex factors and decision-making.

Benefits and challenges of AI

The use of AI in clinical trial data management and EDC development has several benefits:

AI can automate time-consuming manual tasks for a faster and more accurate database.
AI can reduce the possibility of human error while continuously monitoring clinical trial data. This allows data issues to be detected quickly, which can improve data accuracy and safety for trial participants.

However, challenges remain:

Maintaining participant confidentiality while using AI tools can be a significant challenge.
AI systems must be configured and monitored to ensure there is no risk of accessing unauthorized or unblinded data.
Large amounts of data are required for the AI to be properly trained to deliver high-quality results.

Ultimately, the decision must be made of whether the benefits of AI outweigh the challenges.

The pharmaceutical industry is a heavily regulated industry. Validity of AI tools must be established before these tools are deployed for database development and data cleaning. This will ensure the sensitivity and specificity with which the AI tool is expected to perform with a negligible error rate.

Final takeaways

AI will play an important role in many aspects of clinical trials, including and beyond data management. From identifying potential compounds to automating routine tasks, innovating statistical programming, streamlining medical writing, and creating digital twins, we will only continue to see advancements in AI tools in the coming years. AI can be a groundbreaking tool to shorten drug development timelines and improve patient outcomes.

What is an FSP in Clinical Research?

In clinical research, the way trials are managed and supported is critical to success. Functional Service Providers (FSP) have emerged as a preferred model in clinical trial operations to ensure efficiency, flexibility, and quality. But what exactly is an FSP in clinical research, and what sets it apart from other service models?

What is an FSP in clinical research?

A sponsor may not have access to specialized knowledge in specific functional areas and may wish to outsource expertise, while maintaining control over the management of the trial. In the FSP model, a sponsor (typically a pharmaceutical or biotech company) makes a contract with a third-party provider to handle such functional areas, such as data management, biostatistics, clinical monitoring, regulatory affairs, or pharmacovigilance. This optimizes the cost and efficiency of the whole operation, as companies can scale resources up or down to meet the phase and needs of the trial.

While full-service outsourcing hands management of the entire trial over to an external party, FSPs allow sponsors to make the most of the specialized expertise in certain areas while maintaining control over critical trial functions. It is a hybrid approach that is particularly appealing to companies managing complex trials across multiple regions.

The FSP model

Rather than handing over management for the entire trial, the FSP model works by letting sponsors make use of a specific skill set that they may lack internally, outsourcing key functions to experts in those areas. For example, tasks such as collecting, processing, and analyzing data would be passed to an FSP specializing in data management, who could then ensure that all regulatory needs are met.

There are numerous benefits to using an FSP model:

Flexibility: Sponsors can decide which functions to outsource and which to manage internally.
Scalability: Sponsors can adjust which services are being outsourced to meet new requirements as trials progress or expand.
Cost efficiency: FSPs can offer more cost-effective solutions, which is especially useful when managing large or complex trials such as those on a global scale.
Access to expertise: Sponsors can work alongside teams with specialized knowledge in particular functions.

This model ensures that sponsors can make the most of their resources without compromising on quality, which allows for a more focused approach to managing clinical trials.

What should you look for in an FSP partner?

For a clinical trial to be successful, choosing the right FSP partner is crucial. There are several key factors one must consider when evaluating potential partners:

Talent acquisition

Talent acquisition might just be one of the most important aspects of a successful FSP relationship. In the best-case scenario, an FSP partner will have a rigorous recruitment strategy that ensures their team are not only experts in their field but also have experience working within the regulatory frameworks of different regions. This is usually the case with more well-established FSPs and is something to look out for when choosing the right partner.

Effective onboarding

The outsourced team should understand the sponsor’s processes, goals, and expectations, meaning that the success of any FSP partnership is also reliant on a well-structured onboarding process. Effective onboarding guarantees clear communication from the start, helping to streamline collaboration and reducing any potential risk of misaligned objectives over the course of the trial.

Comprehensive FSP model

A comprehensive FSP model shouldn’t just offer functional support; it should enable the partner to integrate seamlessly with the sponsor’s operations. The role of the FSP is to provide tools and processes that enhance trial efficiency, but to do this they must be equipped to support various aspects of the clinical trial lifecycle, whether it’s data management, regulatory submissions, or patient monitoring.

Beyond FSP: Analytics On-Demand

Clinical trials are becoming more complex every day, and sponsors are looking for increasingly flexible options that go beyond FSP offerings. Analytic on-demand services are one such advancement. They involve FSPs providing real-time data analysis and insights that enable sponsors to make informed decisions faster. Real-time data analysis can be used to modify trial parameters mid-study, resulting in faster, more effective outcomes and making these services particularly valuable in adaptive trial designs.

Decision-making in clinical trials is increasingly data-driven, and FSPs that offer analytic on-demand services can help sponsors keep pace in this changing environment. In the age of precision medicine, this service is key, with trials often calling for highly specific and timely insights to guide treatment decisions.

Final takeaways

Today’s clinical research landscape is evolving faster than ever before, and FSPs play a pivotal role in guiding sponsors through the complexities of trial management in this dynamic field. Their specialized expertise, scalable resources, and flexible service models allow sponsors to focus on the strategic aspects of their trials, outsourcing other key functions to reliable and trusted partners. Choosing the right FSP empowers sponsors to streamline operations, reduce costs, and ultimately bring new therapies to market more efficiently.

The Year Ahead for FSP: Open Source, AI, Global Reach, and Cost Efficiency

The Biometrics FSP outsourcing market is evolving faster than ever. Looking back, 2024 was a year of transition for our industry as we put the COVID bubble in our rearview mirror and focused on efficient delivery of our portfolios. Looking ahead now to 2025, Biometrics FSP is on track for continued growth, with a strong emphasis on open-source technologies, global reach, artificial intelligence, and cost efficiency.

Here, I touch a bit on these four areas and share our thoughts on the impact they will have in 2025 and beyond.

Embracing open-source technologies

While the adoption of open-source programming has been slower in the clinical research space, tools like R and Shiny are quickly gaining traction as cost-effective and reliable solutions for data analysis and submissions.

Cytel has been leveraging open-source software for big data aggregation, application development, and validation. We will continue to be a key contributor to the open-source ecosystem and help organizations solve key design and analysis challenges, while offering access to the industry’s top R/Shiny talent pool.

Offshoring hub strategy and cost-effective solutions

The demand for more cost-effective solutions continues to drive the use of offshore resources across the industry. Cytel’s expansion into Eastern Europe and continued growth of our South Africa– and India-based teams positions us well to support our sponsors in reducing costs while maintaining the high levels of quality they have come to expect of Cytel.

Artificial intelligence

AI is revolutionizing the biometrics space by enabling real-time data monitoring, automated code generation, and improving statistical accuracy in clinical trials. By flagging anomalies and potential errors, AI reduces the risk of data discrepancies and enhances overall data quality. AI-powered tools are streamlining biometric services, automating routine tasks and allowing researchers to focus on high-value activities.

Secure data collection and real-time monitoring

Innovations in data collection and real-time monitoring are improving privacy, security, and data integrity. Advanced authentication methods and AI integration are helping ensure the accuracy and confidentiality of data.

Automation is also playing a key role by extracting data from unstructured sources, such as medical records, and reducing human error during data transcription. This further enhances the efficiency of electronic data capture (EDC) systems and boosts the overall reliability of clinical data.

Final takeaways

I’m more excited about 2025 than any year in my career. In an industry that has been criticized for moving too slowly and cautiously, we sit an inflection point for rapid evolution of decades-old models. Change can be exciting and scary all the same. Reach out — myself and our FSP experts are eager to present content and engage with you throughout the year at events such as PHUSE, JSM, SCDM, and more.

P_MACRO: Parameters Extraction from Macros to SAS Dataset

In clinical development, SAS programmers manage, analyze, and interpret clinical data, helping to ensure accuracy, which is essential for regulatory submission and approval. SAS programmers may also create new programs to conduct this work more efficiently and effectively.

SAS has a powerful programming feature called Macros, which allows programmers to avoid repetitive sections of code and to use them again and again when needed. It also helps create dynamic variables within the code that can take different values for different run instances of the same code.

Parameters are local to the macro that defines them. A parameter list can contain any number of macro parameters separated by commas. These macro parameters are variables whose values are initialized when we invoke the macro and provide flexibility to supply different values at each invocation. However, there is currently no automated facility within SAS where a complete list of macro parameters defined for a group of macros can be easily checked

Our solution is P_MACRO, which has been programmed to help programmers refer to the complete list of parameters defined within a macro program within the SAS environment itself. Here, I discuss what P_MACRO is capable of, why it’s needed, how it’s programmed, and its limitations.

What is P_MACRO and what does it do?

P_MACRO is an SAS program that extracts parameter-level information from a group of macros and saves them to an SAS dataset. Once set up, P_MACRO accomplishes several tasks, including:

Extracting parameters from the group of macro programs to the SAS dataset.
Extracting default values along with parameters, if already defined.
Retaining the order of parameters.
Prioritizing main macro information over nested macros.
Providing a common text for macro programs without parameters.
Generating an automated macro call.

Why is P_MACRO needed?

In SAS macros, we have the flexibility to exclude some parameters at invocation and the macro will still execute well, if there is no dependency. But when we do not include the complete list of parameters in a call, it is difficult for the programmer to decide on adding parameters to the existing call when an update/modification is needed. If the programmer is not aware of the complete list of parameters when an update is needed, then they may need to either use debugging options or manually open the macro code and check.

However, we do not have an automated facility within SAS where we can check the complete list of macro parameters defined for a group of macros. Thus P_MACRO, when released for wider group usage, will help programmers gather and refer to the complete list of parameters defined within a macro program in the SAS environment itself. With this, it will be easy to get ahold of the complete list of parameters defined along with their default values and position/order. An automatic macro call for each macro is generated using the information stored in resultant dataset, saving valuable time for the programmer.

Steps involved in P_MACRO programming

Read macro program files to SAS
Extract macro name
Determine macro start and end points
Handle nested macros
Handle macro programs with no parameters
Retain macro parameters position
Bring out default values
Generate macro call

Limitations

Despite its benefits, the P_MACRO program has a few limitations:

%LET statement is used in macro programs to conditionally check and assign a default value for a parameter. Such default values are not extracted through the macro.
SAS Macros has the ability to use the PARMBUFF option and SYSPBUFF to define a macro that accepts a varying number of parameters at each invocation. In such cases, P_MACRO won’t be able to extract any parameters.

Final takeaways

In the SASHELP library, there is a dataset named VCOLUMN that holds detailed information about the metadata of the libraries, datasets, and variables present for that SAS session. This helps programmers to identify/query some of the important information about the datasets/variables for that active session. Like the VCOLUMN dataset, the dataset generated through P_MACRO will help programmers find the list of macros within one folder, with their entire list of parameters in defined order along with default values in one place within SAS. Generating an automated macro call using the resultant dataset would help programmers by having the entire parameters list handy and ready to use as needed.

Interested in learning more?

Eswara Gunisetti will be at PHUSE EU 2024 to present “P_MACRO, Parameters Extraction from Macros to SAS Dataset” on Wednesday, November 13 at 12:00 p.m. We hope to see you there!

Automating Log Checks: A Case for Innovation in Statistical Programming

Innovation in statistical programming is essential for the evolving clinical development landscape, where the demands of managing and analyzing large datasets is growing exponentially. As clinical trials become more data-intensive and require faster timelines, outdated manual processes and inefficiencies in statistical programming workflows can cause costly delays and even impact the integrity of trial results.

One of the biggest challenges faced by statistical programmers is managing the large volume of outputs — tables, graphs, and listings — generated during trials, particularly in later phases. A few years ago, I faced a similar challenge that many programmers dealt with then and continue to struggle with today — the tedious and error-prone process of manually checking log files. To address this, I developed an automated solution that significantly improved the speed and accuracy of the process.

In this blog, I share how this simple, yet innovative solution solved the problem of manual log checks.

SAS vs. R in Clinical Development

With more than 18 years of experience in the clinical research industry, I have worked extensively with SAS. However, in recent times, R has emerged as a groundbreaking tool in data analysis. In this article, I compare the use of SAS and R in clinical development, aiming to determine which tool might be the best fit to meet our requirements.

Career Perspectives: A Conversation with Deqing Pei

In this latest edition of the Career Perspectives series, we are excited to introduce our readers to Deqing Pei, Associate Director Biostatistics in the Functional Service Provider (FSP) department at Cytel.

Can you tell us about your professional journey and what motivated you to pursue a career in biostatistics?

I came to the U.S. to pursue a career as a plant geneticist. During my graduate studies, one of the most valuable tools I used daily to interpret the data generated during experiments was statistics software. Without a thorough understanding of statistics, I sometimes had difficulty interpreting the results. That’s why I decided to dive into more statistics classes and graduated with a double major in Genetics and Statistics. I chose to pursue a career in biostatistics because the work I do in this field helps develop potentially life-changing treatments for patients a few years down the line.

With more than 20 years of experience in academia and research, transitioning to a role at Cytel must have brought about significant changes. What prompted your transition from St. Jude Children’s Research Hospital to Cytel?

My 20 years of experience in academia and research at St. Jude’s Children’s Research Hospital helped me acquire many valuable skills and knowledge, which I treasure to this day. Working for Cytel gives me the opportunity to put these skills into practice on a variety of diverse and dynamic projects in the industry.

Can you share some of the key differences you observed when you joined Cytel?

One of the main differences is that at St. Jude Children’s Research Hospital, we focused on publishing results from the clinical trials in top scientific journals. We didn’t have strong programmer support, so we would act both as a biostatistician and a statistical programmer. At Cytel, I can focus more on my work as a biostatistician with the support of my colleagues in programming.

What is your role at Cytel and what do you like best about it?

I’m an Associate Director of Biostatistics in the FSP team at Cytel, which means that I work within a client’s team. I’ve been assigned to one of the Top 20 Pharma companies, and what I like best about my work is that I can contribute to their studies while working alongside multi-functional teams from both the sponsor and external vendors.

You have co-authored more than 127 papers in top journals such as NEJM, JAMA, NATURE, and others. What advice would you give to biostatisticians looking to publish their research?

The papers I published while working at St. Jude Children’s Research Hospital are the result of multiple functions and teams working together. My advice is that we need to focus on thoroughly understanding the data, helping the clinical team to interpret results correctly, and ensuring the integrity and quality of the clinical trial data. This enables researchers to draw valid conclusions and make evidence-based decisions for publication.

Could you describe some of the advanced statistical methods you have employed in your projects and explain their significance?

In my current position with a sponsor, I am leading two studies. The statistical methods applied in both studies are not incredibly advanced, however the complexity lies in other aspects: the protocol, the operations, and the potential for unanticipated change, requiring flexibility to be able to adapt to variability and change.

Have you worked on any Bayesian or other non-traditional designs? What do you think is the future of Bayesian adaptive designs?

I have not had the opportunity to work on Bayesian or other non-traditional designs. However, I do believe Bayesian adaptive designs offer a flexible and efficient framework for conducting clinical trials and may provide results that are more useful and natural to interpret for clinicians, compared to traditional approaches.

How do you stay updated with the latest advancements in biostatistics and apply them to your projects?

In my previous function, I regularly attended workshops, webinars, and conferences to learn about new developments in the field. Now, I don’t have many of the same opportunities anymore. I usually try to attend seminars held by the client I am working with, and regularly read peer-reviewed journals to stay abreast of new methodologies and applications.

What qualities and skills (soft/technical) do you believe are essential for someone to succeed in a biostatistics role at Cytel?

As a biostatistician at Cytel, you need to be able to understand the nuances of working with databases, data sources, and data collection tools, including their advantages and limitations in answering clinical and scientific questions. You also need strong skills in writing code to manage data as well as be able to implement statistical methods using accurate and efficient coding practices. Additionally, you must be able to conduct a review of literature and background information to identify gaps in scientific knowledge in order to motivate a given project. Motivation actually plays a large role, as you might need to learn and implement unfamiliar statistical methods for a project.

What advice would you give to young professionals considering a career in biostatistics and clinical research?

If you like math and biology and want to make a difference in the world, then biostatistics is a rewarding career with lots of opportunities.

As an employee who works full-time from home, what are your strategies to keep a healthy work-life balance? Do you feel supported in this by Cytel?

I try to make time to enjoy my hobby as an amateur photographer and to work in the garden. These activities help me stay healthy and re-charged, so I can complete my day-to-day work without stress.

Finally, what are your main interests outside of work?

I dream of becoming a wedding photographer after retirement!

Career Perspectives: A Conversation with Karl Karu

In this latest edition of the Career Perspectives series, we are excited to introduce our readers to Karl Karu, Senior Statistical Programmer in Functional Service Provider (FSP) at Cytel. Karl is based in Estonia and joined Cytel in September 2023. Join us as we delve into his refreshing and enthusiastic perspective on statistical programming and the skills it requires, the importance of communication with sponsors, and a work-life balance when working from home.

Can you give us a little background on your career and your professional journey so far? What made you choose a career as a Statistical Programmer after getting a master’s in chemistry?

Half-way pursuing my PhD in Chemistry in 2019, I decided to leave academia to do something that felt impactful in the real world. The pharmaceutical industry is nothing if not impactful! I’ve always enjoyed working with numbers and data, so statistical programming seemed like a good fit—combining my skills with a job that benefits mankind directly. Having limited statistical knowledge at first, I was quite nervous, but that feeling quickly dissipated as I met many successful programmers from diverse educational backgrounds, ranging from physics to agriculture.

What do you like best about your role and why did you choose to work for Cytel?

I am a Senior Statistical Programmer in FSP, where I support the projects of one of our sponsors. Having prior contract research organization (CRO) experience, where I worked with a lot of clients, I thoroughly enjoy the stability and structure of working with a single dedicated sponsor.

In my role, I juggle multiple studies, deadlines, and responsibilities. However, as these are all in collaboration with one sponsor, the studies are built similarly, using the same tools and standards. This allows me to focus on the subject matter and minimize the time spent on trivial tasks. It makes my work so much more rewarding and efficient.

I chose Cytel because it’s known for its statistical software and subject matter expertise, as well as its trusted ability to conduct exploratory analyses and take on more holistic responsibility for clinical trial processes, as my team does. It provides me with unique and exciting opportunities. Additionally, the company is large enough to have solid processes and functions in place, but not too large as to lose its identity and culture.

In your opinion, which skills are most important to be a Statistical Programmer?

People often regard programming as 3D chess, where one must be a computer genius to excel. That could not be further from the truth. Of course, one needs to learn a programming language such as SAS or R, but writing code is just a small part of the job.

I spend significantly more time observing data, reading different source materials, and communicating with other study team members than writing code. Once you understand the aim of the work and are familiar with the data, programming becomes just an afterthought. That understanding is gained through communication and the ability to cross-reference materials such as protocols, analysis plans, and industry standards. Therefore, good communication and personal documentation/organization skills are incredibly beneficial to one’s success as an aspiring statistical programmer.

Have you had any mentors at Cytel? If so, how have they contributed to your professional and personal growth?

I have recently settled into my role at Cytel, having joined in late 2023, but Steven Thacker, Vice President of FSP, deserves a mention in this regard. During my interview process, we happened to meet at the PSI 2023 conference in London. He connected me with a biostatistician, so I could talk about my future position and make a good proactive impression on our sponsor.

Our FSP team consists of incredibly helpful people such as Steven, who like to share ideas and successes.

Could you share a project you have worked on that you feel the proudest of, and why?

Every project I currently work on is a learning experience, cementing my understanding of our workflow and honing my efficiency, but we have not yet reached pivotal milestones in these studies.

My proudest past professional experience was co-authoring an article investigating the relationship between health-related quality of life and overall survival in patients with advanced renal cell carcinoma. I was humbled to work alongside the key opinion leaders in an interdisciplinary team and produce evidence that aids with the care and treatment of renal cancer patients.

As an employee who works full-time from home, what are your strategies to keep a healthy work-life balance? Do you feel supported in this by Cytel?

I absolutely love the home office. I save time, reduce my carbon footprint, and can enjoy bird song instead of car horns in the morning. Cytel, and by extension the FSP team sponsor, fully support a healthy work-life balance in my experience. Except for an occasional late-afternoon call with the US team members, I do not work outside my chosen business hours or even feel any pressure to open my laptop.

One habit that has helped me immensely is to take an extra moment during the day to review my upcoming deadlines and add relevant calendar reminders. It helps me focus during business hours, and I can rest assured I have not forgotten anything when powering off my laptop for the day.

What are your main interests outside of work?

As a proud father of a two-year-old daughter, my current main interest is sleeping. On weekends, we sometimes go hiking together. We live in Estonia, where there are many swamps and bogs with well-developed nature trails and unique nature to enjoy.

To learn more about a career at Cytel, or to explore our openings, click below:

Explore Cytel Careers

FSP Behind the Scenes with Nandan Kothavale, Programming Senior Team Lead

Cytel’s Functional Service Provider (FSP) teams work on exciting projects with biotech and pharmaceutical companies as statistical programmers and biostatisticians. We spoke with Nandan Kothavale, Programming Senior Team Lead, who is based in India and works with a global pharmaceutical company, about the projects she found most impactful and what it’s like to work as a statistical programmer with Cytel’s FSP. Read more »

FSP Behind the Scenes with Jeff Thompson, R Senior Statistical Programmer

Cytel’s Functional Service Provider (FSP) teams work on exciting projects with biotech and pharmaceutical companies as statistical programmers and biostatisticians. We spoke with Jeff Thompson, R Senior Statistical Programmer, who is based in the US and working with a leading global biotech company, about the projects he found most fascinating and what it’s like to work as a statistical programmer with Cytel’s FSP. Read more »

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Transforming data cleaning in clinical trials

Improving database development in clinical trials

Shortening database lock timelines

The need for human involvement

Benefits and challenges of AI

Final takeaways

What is an FSP in clinical research?

The FSP model

What should you look for in an FSP partner?

Beyond FSP: Analytics On-Demand

Final takeaways

Embracing open-source technologies

Offshoring hub strategy and cost-effective solutions

Artificial intelligence

Secure data collection and real-time monitoring

Final takeaways

What is P_MACRO and what does it do?

Why is P_MACRO needed?

Steps involved in P_MACRO programming

Limitations

Final takeaways

Interested in learning more?