In part two of Carol Collins' Economics of Time Series, the focus shift to the economics of capacity; some of the most valuable AI initiatives are not those that simply automate work, but ones that simultaneously reduce delay and unlock capacity.

We tested Verisian’s AI agents on the R Consortium’s Pilot 3, a public ADaM submission package reviewed by the FDA. Given only the SAP, SDTM data and specifications, the agents derived and validated all five ADaM datasets in a single working day, with no human writing code.

"Who checks the AI?" is the question that decides whether this industry's AI era is a success or a cautionary tale. In this article, Andy York, former VP of Statistical Programming at Novo Nordisk and Verisian advisor, discusses the coming role shift and the skills that will matter.

In the first of a four part article series on the Economics of Time, Carol Collins proposes the greatest economic impact from AI often comes not from reducing cost, but compressing time.

Sponsors take months between the last patient last visit of a final pivotal trial (LPLV) and submission, time that sits directly on the critical path for time to market. These months are largely spent on the production and validation of analysis results and the generation of submission documents, before the final submission package is assembled.

Bayer used Verisian AI to automate key submission documentation for a non-CDISC clinical trial, producing submission-ready drafts in under two days and demonstrating how traceability-driven AI can dramatically reduce statistical programming busywork while maintaining regulatory quality.

Andrew York has over 30 years experience as a statistical programmer, and has played a leading role in shaping best practice in the industry. In this episode, Andy shares his unique perspectives on how the role of a statistical programmer has evolved, and how AI, open source, traceability and standards will shape the future of the industry.

Josef von Rickenbach is a true pioneer in the pharmaceutical industry. In 1982, he co-founded Parexel, a company that would go on to define the CRO industry as we know it today. Over the next 36 years as CEO, he led over 40 acquisitions, scaled the company to 19,000 employees, and helped set industry standards that still shape biopharma today.But

Double programming has long been the industry standard for validation in statistical programming, but is it the most effective approach? In this episode, Henning Kuich, Richardus Vonk, Sunil Gupta and Tomás Sabat Stöfsel discuss how code traceability is transforming the validation process.

In this episode, we sit down with David, an expert in R programming for clinical trials, to discuss the evolving landscape of statistical programming. He explores the key differences between R and SAS, the critical importance of backward compatibility, and how organizations can future-proof their R-based systems.

Code traceability captures every dataset imported, created, and exported during the execution of your programs. The resulting dependency graph extends across all files in your analysis, letting you seamlessly navigate from TLFs to raw data inputs in seconds. Get full and transparent data lineage for your clinical trial analyses.

A major milestone in the development of code traceability, the Verisian Tracer now contains and reaches far beyond all traceability concepts defined by CDISC data standards and the define-xml.

In this episode of the Verisian Community Podcast, Shafi shares his over three decades experiences as an expert in statistical programming.

In this episode of the Verisian Community Podcast, we welcome Nijat to share how Lindus Health is building the future of clinical trials.

In this episode of the Verisian Community Podcast, we welcome Sunil Kumar Pusarla to share his journey in the world of statistical programming.

In this episode of the Verisian Community Podcast, we welcome Richardus Vonk to discuss the future of clinical trials.

In this episode of the Verisian Community Podcast, we welcome Ricardo Baeza-Yates, a renowned computer scientist, to discuss the world of Large Language Models

Variable traceability is the holy grail in code tracing: it allows you to see everything necessary to understand a variable without any distracting information.

Existing technology, especially the promises around metadata-driven automation (MDA) and metadata repositories (MDRs), will never adequately address the fundamental problems of the industry. They promise to eventually automate 60-90% of all derivations and TLFs (depending on the complexity of the study).

In this episode of the Verisian Community Podcast, we welcome Daniel Rolo, Director of Statistical Programming at Parexel to discuss the evolving landscape of statistical programming.

In this Verisian Community Podcast episode, we welcome Ritika Aggarwal from Novartis, who shares insights on the evolving landscape of statistical programming.

In this article, we review the Analysis Data Model’s (ADaM) limitations in communicating study analysis decisions, primarily due to its reliance on metadata traceability, and how AI adds to these challenges. As a response, we present the Verisian Validator as a solution by introducing code-level traceability

In this episode of the Verisian Community Podcast, we're thrilled to welcome Mansi Thakrar, a Statistical Programmer at Massachusetts General Hospital. This marks our second installment dedicated to discussing good programming practices, where Mansi shares her insights into what constitutes good statistical code in biostatistics.

In this episode of the Verisian Community Podcast, we welcome Shadab from Merck and Rahul from Johnson & Johnson. Our conversation begins with a discussion on the feasibility and desirability of achieving end-to-end automation in clinical programming. We then delve into the world of generative AI, exploring its potential to revolutionize

In this episode of the Verisian Community Podcast, we welcome Cornelia from Bayer, to discuss good programming practice in clinical programming. Cornelia highlights the critical aspects of writing good SAS code and adapting to languages such as R. Cornelia delves into how Bayer is embracing AI in clinical programming and emphasizes the pivotal role

In this episode of the Verisian Community Podcast, Kyle (Syneos) and Oliver (Bayer) discuss the challenges and opportunities of open source in clinical analysis. They look at the importance of people and communication within the industry and the need for a culture change before embracing new systems.

In the first episode of the Verisian Community Podcast, Tomás and Henning, along with industry expert Sunil, discuss how new technology can improve the world of statistical programming. They look at how the Verisian Validation Engine ensures programmers can guarantee the integrity of their analyses.