In clinical trials we all want faster results and adaptability. These goals can be achieved with trial standardisation and automation. Both approaches aim to streamline processes, but they differ in focus, speed, and flexibility. Let’s explore the concept of standardising studies, its relationship with automation, and what each approach can offer.
At its core, study standardisation creates one clear way to run every study. This involves aligning to industry standards, such as CDISC, or opting for non-CDISC frameworks, depending on the organisation and project requirements.
CDISC Workflow
Non-CDISC Workflow
The central flow of study processes remains consistent with industry standards, ensuring validity and compliance. However, input variations between studies are the primary challenge. Inputs such as protocol-specific elements, study design, primary and secondary endpoints, raw datasets, or instructions may differ across studies, while implementation guides (IGs) often remain consistent.
When input variability is minimised, each step of the process becomes identical, teams move faster with fewer hand-offs, and errors are reduced due to the predictable nature of the workflows.
This makes standardisation a powerful strategy for delivering speed and accuracy, particularly for repeatable and structured studies. Having a set framework to carry on a study makes it less time consuming to replicate it in different studies but is less flexible at times.
While standardisation optimises consistency, automation introduces adaptability. Automation tools and workflows are designed to handle input variability across different studies. With the use of wrapper macros, SDTM code generator, and aCRF generation tools, these pillars of automation speed up the process and allow for flexibility. This adaptability allows teams to quickly adjust to new or modified requirements, streamline processes without the need for manual intervention, and handle a broader range of study designs, accommodating unique client needs.
Automation thrives in dynamic environments, offering flexibility without compromising on efficiency. It focuses on adapting to the inherent differences in study inputs, enabling teams to manage diverse projects effectively.
While both approaches aim to enhance operational efficiency, they come with specific trade-offs. For example, standardisation prioritises speed and accuracy at the cost of flexibility, making it ideal for projects with consistent requirements, where uniformity ensures faster delivery. Automation, on the other hand, sacrifices some speed to adapt to variability. This makes it well-suited for complex projects or clients with unique needs, allowing for tailored solutions.
The choice between the two depends on the nature of the studies and customer expectations. For instance, a client requiring large-scale, repetitive studies may prefer standardisation, while a client with diverse, evolving study designs may benefit more from automation.
Neither standardisation nor automation is inherently superior but rather complement each other in addressing different scenarios. If both approaches are used together, it strategically can reap the benefits of efficiency and adaptability, depending on the project’s demands.
1. Standardisation for Scalable, High-Volume Clinical Trials
Standardisation is perfect for scenarios where large volumes of similar studies need processing, strict adherence to industry standards is critical, and speed and predictability are key.
2. Automation for Flexible, Customisable Trial Workflows
Automation excels in cases where study designs are diverse or frequently changing, customisation is a priority for the client, and rapid adaptation is necessary.
The objective of this case study is to demonstrate the process of data analysis and reporting for a parallel study conducted on a dummy dataset of 10 patients. The focus is on creating standardised ADaM datasets and developing TFLs (Tables, Figures, and Listings) for safety analysis. While the study is based on hypothetical data, it simulates real-world workflows.
We tried to standardised the codes for safety outputs in Phase I studies and used our tools to generate them, which helped us complete the study in less than the anticipated time.
Data was collected in line with CDISC standards and organised into SDTM-compliant domains.
SDTMs created: DM, AE, VS, LB, EG, MH
SDTMs were created using minimal parameters. This helped us create an outline of SDTMs which we could replicate on Phase I studies and with minimal effort replicate for more paramaters.
ADaM datasets were created to facilitate statistical analysis.
ADAMs created: ADSL, ADMH, ADLB, ADAE
1. Standardisation
Using standardised ADaM structures streamlined the analysis and ensured compliance with regulatory expectations.
2. Efficiency
Automation of derivations and macro-based TFL creation significantly reduced processing time while maintaining high quality.
This case study demonstrates the application of standardised processes, including ADaM datasets and TFL development, in a hypothetical study. By using CDISC-compliant standards, the study ensured consistency, traceability, replicability, and ease of interpretation. Add automation and you finish sooner, with fewer errors. While this example used dummy data, the methodologies are applicable to real-world studies, highlighting the importance of standardisation for efficient reporting in studies with the help of automation for speed and flexibility.
Quanticate's statistical programming team help turn complex trial data into clear, real-time insights you can act on, with secure platforms, wrapper macros, SDTM code generators and aCRF generation tools, upholding data integrity and regulatory compliance every step of the way. To learn how our expertise can optimise your study execution and outcomes, submit an RFI today.