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:

1. The combination of open source with commercial software solutions
2. 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.

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