Verisian Blog

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.

In clinical trials, the path from raw data to TLFs is complex and requires a deep understanding of both the data and its transformations. The Verisian Results Validation Module solves these challenges by streamlining the review of TLFs, the code required to derive them, and underlying data at SDTM, ADaM, and TLF level.

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.

We are building a solution that fully automates define-xml generation. Based on our code traceability, we have developed an AI system that auto-generates computational method descriptions in natural language, in addition to providing variable-level traces for every single CDISC variable.

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 tutorial, we explain how to create a free trial, set up a study, run an analysis, and explore the results using the Tracer or Log QC module.

In this tutorial, we explain how to use the Verisian Tracer to explore a log analysis and make use of the file, library, and dataset traceability.

Root cause analysis (RCA) in clinical trial analyses, especially when using macros in SAS, is difficult and time consuming. We here describe how Verisian’s Tracer drastically improves the speed with which root causes can be identified and resolved by providing full traceability across programs and the dataset lineage of your study.

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

Root cause analysis when debugging SAS logs is manual, time consuming, and error prone - even more so when SAS macros are involved. Yet it is a crucial and required QC process for clinical trial analyses. In this article, we describe how Verisian’s Validator drastically improves the speed an

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.

After a lot of hard work by the Verisian team, I’m so excited to announce the first release of the Verisian Validator! With the Validator, we want to change the way SAS execution logs are analyzed, offering an immersive experience to help programmers grasp all important log messages,

I'm so excited to launch the Verisian Community, dedicated to statistical programmers, statisticians, data managers, and all professionals working with clinical trial data. Our mission is to cultivate a global forum for the exchange of ideas, knowledge, and best practices, empowering you to contribu