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A Preview of Cytel’s Contributions at PHUSE EU 2025

I can’t believe it has already been a year since we wrapped up PHUSE EU Connect 2024, and in two weeks we will be gathering another exciting PHUSE EU Connect conference, only a few kilometers from Heidelberg, where everything started twenty years ago with the very first PHUSE event. I was one of the couple hundred lucky attendees and now, twenty years later, I have the great honor of supporting Jennie McGuirk and Jinesh Patel as Conference Co-chair for this year’s edition.

With a promising agenda featuring about 190 presentations, 34 posters, 9 hands-on workshops, 2 panel discussions, and 3 inspiring keynote speakers, this year we are going to the city of Hamburg for the 21st PHUSE EU Connect. The agenda is full of topics looking toward the future, with about 40 talks and posters referring to AI in their titles, and once again open source will be the confirmed leitmotif.

Cytel will make a significant contribution this year, perhaps more than ever, with six presentations, one poster, active participation in both panel discussions, and co-chairing the “Scripts, Macros and Automation” and “People Leadership & Management” streams.

Monday topics: Agile code writing, extracting metadata from R OOP functions, and leadership

The week kicks off on Monday with Kamil Foltynski, who will present “Overcoming Challenges in Collaborative Spreadsheet Editing with Shiny, SpreadJS and JSON-Patch” in the Application Development stream at 11:30 am. Kamil will provide a technical deep dive into enabling real-time spreadsheet editing within Shiny applications, using tools such as SpreadJS, sharing key lessons learned so far. Following Kamil’s presentation, Eswara Satyanarayana Gunisetti, will present “Micro-Decisions, Macro Impact: The Role of Agile Thinking in Every Line of Code” in the “Coding Tips & Tricks” stream at 12 pm. See his recent blog on the topic. Eswara will share how an agile “mindset” can positively influence the way we write code.

In the same stream, a few hours later at 2 pm, another colleague Edward Gillian, in collaboration with Sanofi, will present “Risk.assessr: Extracting OOP Function Details,” discussing strategies for extracting metadata from R Object-Oriented Programming functions. Prior to Eswara and Edward’s sessions, at 1:30 pm, Kath Wright, will moderate the Interactive People Leadership & Management session “Invisible Glue: Trust, Influence and The Architecture of Teamwork.” With this live workshop, attendees will engage in practical exercises to learn how to identify barriers to trust, evaluate influence dynamics, and apply evidence-based strategies to strengthen collaboration in both physical and virtual environments.

Tuesday topics: Industry trends, extracting macro usage and dependency information from SAS programs, and integrating ECA data into CDISC-compliant datasets

Tuesday also brings two presentations and one poster. Right after lunch at 1:30 pm, Cedric Marchand will join other industry leaders in the panel discussion “Reimagining Statistical Programming: AI, Standards & the Talent of Tomorrow.” The panel will explore how current industry trends, such as AI, open source, and the evolution of data standards, will influence the next generation of statistical programmers.

The afternoon continues at 4 pm with my young and talented colleague Marie Poupelin, who will present “From Zero to Programming Hero: How Internships Shape Statistical Programmers in a CRO” in the “Professional Development” stream. Marie is a great example of the success of our internship program, and she will share her journey from having “zero” statistical programming experience to becoming an industry-ready programmer. Thirty minutes later, at 4:30 pm, Guido Wendland will present “Which Macros Are Used in the Study?” in the “Scripts, Macros and Automation” stream, a stream co-led this year for the first time by my colleague Sebastià Barceló. Guido will discuss techniques to extract macro usage and dependency information from SAS programs; this is particularly useful for identifying potential issues or estimating the impact of macro updates.

Later, in the traditional Tuesday evening poster session, you can join my colleague Cyril Sombrin in discussing “Our Journey in Integrating External Control Arms (ECAs) and RWD for Rare Disease Trials.” There you can discuss real-world case studies on integrating ECA data into CDISC-compliant datasets, exploring the unique challenges and solutions when aligning real-world data with CDISC standards.

Wednesday topics: Real-time spreadsheet editing within Shiny applications and real-time validation and streamlined submissions

On Wednesday at 12 pm, Hugo Signol, another young talented Cytel statistical programmer and a product of our internship program, will present his talk “From XPT to Dataset-JSON: Enabling Real-Time Validation and Streamlined Submissions.” Building on Cytel’s experience from CDISC Dataset-JSON-Viewer Hackathon, Hugo will demonstrate a Shiny application that supports interactive exploration and real-time validation through API-based checks.

Meet us there!

Cytel will be at Booth 9 at the conference, where you can engage in discussions with our team or meet any of us throughout the week.

I hope I didn’t miss anyone, or anything! We look forward again to reuniting with colleagues and friends from around the world and meeting new acquaintances.

See you all in Hamburg!

Blending Power and Flexibility: How AI-Generated R Code is Reshaping Clinical Trial Design

In today’s fast-evolving clinical research landscape, designing robust and efficient trials is more critical than ever. As statistical designs grow in sophistication, biostatisticians are increasingly relying on both commercial platforms and open-source tools to meet unique modeling needs. But this hybrid approach also comes with challenges, particularly for those new to advanced simulation software or lacking programming experience.

At Cytel, we’ve been exploring how artificial intelligence (AI) can help bridge this gap. At the 2025 Joint Statistical Meetings (JSM), we will present on our latest innovation: AI-powered R code generation for clinical trial design, a feature embedded in our East Horizon™ platform. This assistant, called RCACTS (R Coding Assistant for Clinical Trial Simulation), represents a significant step forward in making custom trial design faster, more accessible, and more reliable.

Why talk about this now? The open-source imperative

While commercial clinical trial design software offers rapid design development through validated and user-friendly workflows, it doesn’t always address the full complexity of real-world problems. Trial statisticians often face challenges in areas such as oncology, rare diseases, and adaptive designs that require tailored statistical tests, unique outcome generation models, or alternative randomization techniques.

This is where open-source tools like R become invaluable. R allows statisticians to write custom code to simulate complex trial designs, perform Bayesian analyses, or integrate evolving regulatory guidance. Over the years, a vibrant ecosystem of R packages has emerged, offering a high degree of transparency, flexibility, and academic rigor.

Yet this flexibility comes with trade-offs: code development can be time-consuming, error-prone, and requires significant programming expertise. As a result, many biostatisticians find themselves switching between validated commercial workflows and custom R functions, leading to a process that is often fragmented and inefficient.

Recognizing this, Cytel’s East Horizon platform has introduced R integration points, enabling users to inject custom code directly into validated simulation workflows. This integration delivers the best of both worlds: the speed and structure of commercial software with the creativity and control of open-source.

Enter AI: Speed, simplicity, and smarter coding

Our next logical question was: can AI make this process even easier?

The answer, increasingly, is yes. With recent advances in generative AI, particularly large language models (LLMs), it’s now possible to assist in the generation of R code for simulation-based design tasks. At Cytel, we’ve harnessed OpenAI’s GPT-4o via API, securely deployed within Microsoft Azure, to create RCACTS, a coding assistant purpose-built for biostatisticians.

Unlike generic AI tools that produce standalone R scripts, RCACTS generates R code specifically tailored for the East Horizon simulation engine. It ensures that the generated functions:

Match expected input/output structures,
Include pre-defined parameters as shown in our internal statistical package CyneRgy,
Are immediately ready for integration and testing within a live trial design workflow.

With RCACTS, users can simply describe what they want in plain English and receive functioning R code that can be integrated into East Horizon.

Who benefits? Everyone from newcomers to experts

One of the major advantages of this AI-enhanced workflow is lowering the barrier to entry. For a new user unfamiliar with Cytel’s R integration or syntax requirements, writing compatible code from scratch can be daunting. RCACTS significantly reduces the learning curve by providing validated function templates, sensible defaults, and clear parameterization, all supported by generative AI.

At the same time, experienced statisticians benefit by spending less time on repetitive coding tasks, debugging, or remembering function signatures. This allows them to focus on higher-level design questions, such as: What analysis method is most robust? How sensitive is the design to different outcome distributions? What dropout patterns pose the greatest risk?

Our assistant supports a wide range of trial design elements:

Simulating patient responses: Binary, Continuous, Time-to-event, and Repeated-measure endpoints.
Analyzing simulated data: Statistical analysis for these endpoints.
Randomization: Flexible randomization of patients across treatment groups.
Enrollment and dropout modeling: Custom mechanisms for realistic patient enrollment and dropout scenarios.
Treatment selection: Supporting multi-arm multi-stage (MAMS) trial designs.

Balancing innovation with responsibility

Of course, like any AI solution, there are caveats. AI-generated code must be carefully reviewed for correctness, appropriateness, and regulatory readiness. RCACTS includes a built-in testing functionality to flag structural or syntactic errors, but statistical validation remains the user’s responsibility. Also note that all data interactions adhere to Azure OpenAI’s stringent data protection policies to ensure security and compliance.

There’s also a broader concern: will over-reliance on AI limit the creativity and deep statistical thinking that define our profession? At Cytel, we view AI not as a replacement for expertise, but as a tool to amplify it. Our goal is to give statisticians more time and mental space to explore, iterate, and innovate rather than reduce them to prompt engineers.

Looking ahead

The future of clinical trial design lies in intelligent integration: combining the strengths of validated commercial tools, flexible open-source frameworks, and AI-powered coding assistance. With East Horizon and RCACTS, we believe we’re building the blueprint for this future, with a platform that supports both scientific rigor and operational speed.

As the field continues to evolve, biostatisticians will need tools that not only keep up with complexity but also support creativity, collaboration, and efficiency. AI-generated R code, embedded within a powerful simulation engine, is one such tool and is already transforming how we approach design flexibility in clinical trials.

Catch us at JSM 2025 to learn more about how AI is transforming the future of clinical trial design within Cytel.

Addressing Uncertainty in Survival Studies

As we have highlighted in prior blog posts, the ability to augment design characteristics with custom R code is especially relevant to the ever-evolving therapeutic area of oncology. As regulatory guidelines are routinely adjusted to comply with clinical practice and current research, oncology study simulations often require specific analysis approaches and/or patient outcome data generation methods to conform to changing evidence thresholds and to create more realistic simulated scenarios.

Defining parameters and addressing uncertainty in survival studies

As in all clinical studies, there is a degree of uncertainty in assessing the treatment effect in trials employing a survival endpoint. For these types of studies, the timing of a patient’s event is typically sampled from a distribution with known parameters such as an exponential distribution with a median time value for each arm in the trial. The assumptions employed in defining these parameters are based on some prior knowledge derived from previous studies, meta-analyses, or other experience of the clinical development team.

Why does this matter?

When prior data is scarce, both the assumed distributions and median values are highly uncertain, and may lead to trials that are more costly, longer in duration, and/or with a diminished probability of success. It is therefore important for product development teams to derive meaningful values for these inputs in the design stage of clinical studies.

Custom R coding for oncology designs

One approach to derisking such trials is to simulate patient data based on a distribution of possible median time values for each arm rather than one single value. This accounts for the fact that the true value is difficult to estimate before the trial begins and removes the need to select just one value. This approach also provides confidence in additional investment based on more realistic assumptions.

To employ this design approach, we propose using flexible R code in conjunction with Cytel’s East Horizon^TM platform to customize the way in which the data for each simulated patient is generated. We propose modifying the response generation’s algorithm to consider a distribution of true treatment effects rather than one single value assumption. The probability of success becomes more conservative but also more informative as the simulation is more realistic of the trials about to take place. This gives the product development team more confidence in trial execution and a better estimation of trial costs and length.

Want to learn more?

Watch J. Kyle Wathen and Valeria Mazzanti’s webinar “A Closer Look at Assurance: Sampling Patient Outcomes from Prior Distributions to Account for Uncertainty in Response Scenarios”:

Harnessing AI-Powered Tools for Clinical Trial Design Coding

The global move towards AI-powered tools is sweeping across the life sciences industry. In particular, the roles biostatisticians play in both clinical trial design and programming lend themselves to AI-based innovations.

Earlier this month, Cytel launched its first AI-driven solution for clinical trial design code generation and joined the artificial intelligence revolution. This innovation is predicated on several years of research and development, coupled with the recent maturing of AI-focused service providers. The solution is designed for optimal functioning within the East Horizon platform and intended to enhance R integration functionalities that are now embedded within our software.

What makes Cytel’s AI-powered R coding assistant unique?

The coding assistant generates R code with required parameters for East Horizon. Unlike generic AI-based coding tools that generate standalone R scripts, this solution ensures the generated code includes function templates, expected arguments, and input variable names; is structured for direct integration into East Horizon’s simulation engines; and is aligned with industry best practices for regulatory-compliant clinical trials. In addition, the coding assistant is purpose-built for biostatistics and clinical trial design.

General-purpose AI tools do not innately relate to adaptive trial designs, survival analyses, or clinical trial randomization. Cytel’s AI-powered R coding assistant allows biostatisticians to generate custom statistical tests beyond software-native options; perform advanced patient data modeling such as Quasi-Poisson, longitudinal outcomes, etc.; and allows for alternative randomization and drop out modeling methods.

Finally, the coding assistant is embedded within an industry-standard solution for trial design. The solution ensures compatibility with East Horizon’s statistical engine, generating code that is formatted correctly and validation-ready.

How does the solution work?

Users interested in augmenting their trial design simulation work can select the R integration features within the software and gain access to the coding assistant. Users then enter prompts in natural language to illicit a response. The user can review the response, iterate and refine with additional queries, and modify the code to fit the task at hand. Once refined, the code can be employed in simulation runs for additional validation and debugging.

Why does this matter?

The AI-powered R coding assistant in East Horizon enables biostatisticians to generate complex R code instantly; customize trial simulations with precise statistical methods; and reduce manual coding errors and speed up model validation.

Custom R coding for oncology designs

The ability to augment design characteristics with custom R code is especially relevant to the ever-evolving oncology area of study. As regulatory guidelines are routinely adjusted to comply with clinical practice and current research, oncology studies often require specific analysis approaches and/or patient outcome data generation methods to conform to changing evidence thresholds. For example, the testing method and analysis type chosen for a specific design can be highly sensitive to the underlying distribution of the data. Therefore, simulating designs with a variety of analysis types can help design studies that are robust to a variety of possible data distributions.

With this in mind, using commercial software to generate patient outcome data through simulation takes full advantage of the software’s native workflows and computing power. These data are then analyzed against a variety of analysis types using R code augmentation. This approach to analysis variation also lends itself to advanced Bayesian tests, affording biostatisticians maximum flexibility.

Want to learn more?

In our recent webinar, “Evaluating Different Analysis Options for Your Oncology Study Design by Combining East Horizon and R,” J. Kyle Wathen and Valeria Mazzanti discuss clinical trial design using a combination of R coding and Cytel’s proprietary statistical software, with a focus on analysis testing variations:

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.

Driving Innovation in Clinical Trial Design: Open Source, Commercial Software, and AI in 2025

As we usher in a new year, we reflect on 2024’s prominent trends in simulation software for clinical trial design that will continue to drive innovation in the coming year. The two main areas of growth and innovation we see taking the lead in 2025 are:

The combination of open source with commercial software solutions
The increasing use of AI to generate open-source code and augment clinical trial design

Commercial software: Confident and quick design capabilities

Commercial software remains a common and popular choice for clinical trial design, with many sponsors opting for these tools. This choice allows for confident and quick design through validated workflows and pre-coded and verified design types. As an accepted choice with a wealth of trial design options, biostatisticians can easily and quickly design and compare a variety of trials. Furthermore, users enjoy access to expert professional support in addition to frequent software releases that ensure updates to methodologies and design types.

Open-source code offers a high degree of flexibility

Although commercial software provides numerous benefits to biostatisticians, there are also drawbacks to this choice in isolation. In a complex scientific field, biostatisticians often encounter idiosyncratic problems that require unique and custom solutions. In these cases, validated commercial software may prove insufficient, and custom code must be developed to address the problem at hand. In fact, this need for flexibility is at the root of the rise of open-source software for custom coding using industry-accepted languages like R, Python, or Julia. These languages afford biostatisticians a degree of creativity in their work and go hand-in-hand with the collaborative nature of this highly academic field. Over the years, many code packages have been developed and shared as solutions to unique design aspects, helping to drive and shape industry trends.

However, with this near-limitless flexibility come several drawbacks. Vetting or developing a bespoke solution can be complicated and resource intensive. Time is required for collecting requirements, writing code, testing, and validating a custom open-source design option. This approach relies on a set of expertise in both software development and statistical methodology. While biostatisticians have deep knowledge and experience in statistics and clinical trial design, they are not typically trained in best practices for software development and programming. These best practices are crucial in developing reliable, robust solutions that can easily be shared with others and that apply to a wide array of trials. Finally, the results derived from open-source code require additional resources for both design selection and communication of results, in the context of a multidisciplinary team. The biostatistician’s attention is thus diverted from providing valuable strategic input to the clinical development team towards software development and implementation tasks.

Combining open-source code with commercial software

Acknowledging these challenges, the industry is quickly adopting a combined-capabilities approach that incorporates the established, validated backbone of commercial software with the added creativity afforded by open-source code. This approach allows biostatisticians to augment elements of the design such as the choice of analysis type, statistical test, or the distributions used to generate various design inputs, without the need to code an entire design. In addition, clinical trial design professionals benefit from the cloud computing power embedded in some commercial software solutions, eliminating the need for maintaining an expensive internal computational grid. We believe that this integrated future of study design harnesses the benefits of both commercial software and open-source solutions while limiting the drawbacks experienced with each approach individually.

The use of artificial intelligence in generating code for clinical trial design

Along with the intensive use of R and other coding languages, we believe that we will see increased interest in using AI solutions for a variety of clinical trial design and execution activities. These applications of AI may include data transformation and cleaning; statistical analysis; protocol writing; clinical data reporting; trial management practice; and efficient code generation and validation for clinical trial design. For the latter, AI solutions powered by Large Language Models (LLMs) can be harnessed to produce analysis-ready custom code based on project specifications. Indeed, over the past few months, Cytel has introduced an AI-driven coding assistant in its newest clinical trial design software to augment study designs with novel approaches via custom code. This approach holds several advantages, among them: the ability to generate code faster; the potential for efficient code validation and editing; and the ability to generate code using natural language prompts.

With the great promise that such tools hold, there are also potential drawbacks and concerns expressed by biostatisticians working in the field. AI-supported code generation requires close review by trained coders to ensure the code created using these tools is sound and applicable to the purpose for which it was created. While code generated by AI can save considerable resources, it requires close supervision and review for validation and application in practice. Over-reliance on code-generation tools may, over time, change the way in which statisticians think through complex coding problems, and limit creativity in this field.

Final takeaways

The landscape of clinical trial design is poised for significant advancements in 2025, driven by the integration of commercial software and open-source solutions, as well as the innovative application of AI for code generation. By leveraging the strengths of commercial software — validated workflows, expert support, and computational power — and combining them with the flexibility and creativity of open-source coding, biostatisticians can overcome traditional challenges and design trials more efficiently. Furthermore, AI-powered tools promise to streamline the generation, validation, and customization of code, empowering teams to focus on strategic decision-making and innovation. These trends signal a promising era of collaboration, efficiency, and enhanced capabilities in clinical trial design.

Cytel’s East Horizon Platform now includes open-source integration points, allowing users to inject custom analysis types, statistical tests, and patient outcome generation into existing software workflows. In addition, the software includes an advanced AI-driven coding assistant that can generate compatible custom R code using plain language queries for integration in study designs. These new features, in combination with Cytel’s advanced trial simulation tools and cloud computing capabilities offer a potent, comprehensive solution for clinical trial design and optimization.

The Journey into Open Source … So Far!

Written by Sebastià Barceló, Malte Stein, and Angelo Tinazzi

Open source has been a leitmotif in our industry for many years now, but its adoption poses a number of challenges. At Cytel, our journey into open source began a couple of years ago. Since then, we have focused on building a dedicated Statistical Computing Environment (SCE), defining new processes, and developing new tools to support these processes. Additionally, we also contributed to industry initiatives such as the R {admiral}.

This year, PHUSE-EU will feature a dedicated stream, Open-Source Technology, where presenters will share their experience with open-source technology adoption. In this spirit of collaboration, we will be contributing with two presentations, both addressing critical aspects:

The co-existence of R and SAS in the same SCE
The risk assessment of R packages

Integrating RStudio POSIT and SAS in the same environment

Our new SCE integrates RStudio POSIT and SAS Grid across both Windows and Linux servers. The integration was designed to create a unified and efficient environment for data analytics, leveraging both SAS and POSIT’s capabilities.

The integration was complex and presented several obstacles and surprises along the way. For instance, we encountered compatibility issues, particularly around data access and permissions. To address these, we implemented dual protocol drive, enabling real-time data sharing across platforms, and the use of Git as a version control system, which allows us to maintain and publish content in Connect in a more robust and secure way.

Additional challenges in managing this SCE include balancing security with usability for internal and external access to POSIT Connect and optimizing R package management.

Figure 1 illustrates the final infrastructure.

R packages risk assessment

Installing and using R packages in the SCE requires assessing the risks associated using these packages. These packages are typically accessed through CRAN, the primary source for R packages developed by various organizations and individuals. Risk assessment is especially critical in industries like pharmaceuticals, where strong compliance requirements (e.g., GxP), necessitate that packages are well maintained, documented, and, after all, reliable.

A key aspect of the risk assessment is the collection of packages metadata, enabling us to classify and assess the reliability of all potential packages we will want to make available in our SCE.

At Cytel, we applied a comprehensive assessment approach by extracting metadata from R packages. We began by evaluating various techniques, such as APIs and web scraping, and compared our approach with the R riskmetric package. This comparison highlighted limitations in conventional methods, which often only address the latest package version. As a result, we enhanced our metadata extraction process.

Interested in learning more?

If you are attending the PHUSE-EU in Strasbourg from November 10–13, do not miss Sebastià and Malte’s poster and presentation, where the co-existence of R and SAS and our approach to extracting metadata from R packages will be discussed in more detail:

“Bridging Platforms: Integrating RStudio POSIT and SAS Grid in the Same Environment”

Cytel presenters: Sebastià Barceló and Malte Stein

Tuesday, November 12, at 5:30 p.m. (Poster Session – PP28)

“Unveiling R Package Risk Assessment: A Comparative Analysis of Metadata Extraction”

Cytel presenters: Malte Stein and Sebastià Barceló

Wednesday, November 13, at 1:30 p.m. (Open-Source Technology Stream – OS14)

Angelo Tinazzi will moderate the Scripts, Macros and Automation stream, which will also cover some open-source experiences from other organizations.

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A Preview of Cytel’s Contributions at PHUSE EU 2025

Monday topics: Agile code writing, extracting metadata from R OOP functions, and leadership

Tuesday topics: Industry trends, extracting macro usage and dependency information from SAS programs, and integrating ECA data into CDISC-compliant datasets

Wednesday topics: Real-time spreadsheet editing within Shiny applications and real-time validation and streamlined submissions

Meet us there!

Blending Power and Flexibility: How AI-Generated R Code is Reshaping Clinical Trial Design

Why talk about this now? The open-source imperative

Enter AI: Speed, simplicity, and smarter coding

Who benefits? Everyone from newcomers to experts

Balancing innovation with responsibility

Looking ahead

Addressing Uncertainty in Survival Studies

Defining parameters and addressing uncertainty in survival studies

Why does this matter?

Custom R coding for oncology designs

Want to learn more?

Harnessing AI-Powered Tools for Clinical Trial Design Coding

What makes Cytel’s AI-powered R coding assistant unique?

How does the solution work?

Why does this matter?

Custom R coding for oncology designs

Want to learn more?

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

Embracing open-source technologies

Offshoring hub strategy and cost-effective solutions

Artificial intelligence

Secure data collection and real-time monitoring

Final takeaways

Driving Innovation in Clinical Trial Design: Open Source, Commercial Software, and AI in 2025

Commercial software: Confident and quick design capabilities

Open-source code offers a high degree of flexibility

Combining open-source code with commercial software

The use of artificial intelligence in generating code for clinical trial design

Final takeaways

The Journey into Open Source … So Far!

Integrating RStudio POSIT and SAS in the same environment

R packages risk assessment

Interested in learning more?

“Bridging Platforms: Integrating RStudio POSIT and SAS Grid in the Same Environment”

“Unveiling R Package Risk Assessment: A Comparative Analysis of Metadata Extraction”

Cytel will be at Booth #6! We hope to see you there!