The bigger picture

Understanding the value of statistical computing environments

Statistical computing environments (SCE) are the backbone of clinical trials, especially as organisations become more data-driven.

Researchers are able to collect more data from more participants equipped with wearables, utilise new unstructured data sources and simulate responses to potential new drugs without the risk of harm to humans.

Every action and reaction, from the lab to clinical trials, are recorded, analysed, and monitored for safety, efficacy, and compliance within the SCE, during each stage of drug development.

Yet senior leaders in the business aren’t always aware of the breadth, granularity and value of the data, nor are they cognisant of their current platform’s capabilities and how it could be improved.

When an SCE is confined to R&D and biostatistics teams, it’s easy to miss the bigger picture. At the very least, senior leaders must be confident their system is running optimally using the latest technology and that decisions are based on transparent and auditable analytical processes, which are checked for biases.

They also need to consider how the SCE will support increasing volumes of data, and how it fits into their wider digital transformation strategies.

The data contained within the system can also deliver value at a strategic level. While the C-suite might not need to understand every model with the same detail as the researchers, it can still derive valuable information from them.

Insights from the SCE could, for example, allow the C-suite to identify underserved patient groups faster, and aid the development of new treatments for rare or novel diseases.

SCEs have developed enormously over the past decade. The most effective ones are cloud-based, which allows for more flexibility and scalability, and use machine learning (ML) to optimise analytics.

All this means that vast amounts of data from clinical trials and other sources can be analysed rapidly using validated, proven and modern statistical methods. Information is then presented in a format that makes sense to the decision-maker, using graphs, charts and other visualisations.

The SAS programming language has been a staple of SCEs for a long time now. More recently, we’ve been developing ways for more people in the organisation to get value from the data. With no coding background, users can model different scenarios such as improving operational efficiency in trials and looking for signals across multiple studies.

All these business decisions are critical in enabling pharma companies to innovate at speed, ensuring that clinical trials can be run on a larger scale, and completed quicker, in line with ethical and regulatory requirements.

Better business decisions support the development and delivery of new treatments and a leaner supply chain – which help to control costs for healthcare providers and make drugs more widely available to patients.

The fact that SCEs have remained siloed in R&D for so long is nobody’s fault, since older systems weren’t always user-friendly.

All that is changing as we see the democratisation of analytics taking hold in more organisations, empowering users at all levels to access clear and actionable insights.

The proprietary data within SCEs is one of the most valuable assets a pharma company owns – now they have the means to apply it to more decisions.

Kayt Leonard, Global Health Care and Life Sciences Strategic Advisor at SAS. Go to sas.com