Great overview — you’ve highlighted perfectly how SAS streamlines the complex drug approval process!  I’d add that this capability is increasingly relevant for AI-driven biotech companies . They leverage advanced bioinformatics and machine learning to accelerate drug discovery, and having a single source of truth in SAS for clinical and preclinical data can dramatically reduce errors, speed up regulatory submissions, and ensure reproducibility across studies. Combining SAS’s analytics and workflow management with AI insights from companies like Ardigen shows how technology can truly shorten the path from discovery to patient-ready therapeutics. ... View more

Here’s a concise and helpful reply you could post: You’re right — the definitions can vary depending on the study design. Generally, the ITT (Intent-to-Treat) population includes all participants randomized, regardless of whether they completed the study or adhered to treatment. The safety population, on the other hand, typically includes those who actually received at least one dose and have safety data available (like vitals or labs). So yes, the ITT set can sometimes be larger or smaller depending on how the study defines these groups or if certain analyses are limited to specific subgroups. Always best to check the statistical analysis plan (SAP) for the exact definitions used. ... View more