SAS truly is the backbone of clinical data management, especially when transforming messy trial data into regulatory-compliant CDISC formats that tell a clear story from molecule to medicine. It’s amazing how statistical programmers like “Maya” are now central to ensuring both scientific accuracy and patient safety. Also, great timing — companies such as Ardigen are expanding this frontier by integrating AI and precision bioinformatics into early-stage drug discovery, bridging the gap between data analytics and clinical insight. The future of trials will likely combine SAS’s reliability with AI-driven prediction models — a powerful mix for next-generation therapeutics. ... View more

Great write-up — love the blend of wet-lab reality and hard lessons in data engineering. Turning a messy mix of plate reader Excel files into a single, validated dataset for USDA submission is real craftsmanship. Your approach — standardize input with a template, enforce “raw only” acceptance, and iterate the SAS ETL for robustness — is exactly what a regulated lab needs. If I may, a few suggestions that helped me in similar projects: modularize reusable pieces into macros, add automated validation/QA steps that fail loudly, document the mapping rules inside the code, use version control for templates and code, and consider server scheduling for repeatable batch runs. Happy to review a snippet of your SAS code or sketch macros for plate mapping if you want — this is the kind of problem I love.  Quick practical tips (bite-size) Macros for plate layout — create one macro to expand A1–H12 into well IDs and attach dilution/lot metadata; reuse across plate sizes. Template validation — a small SAS check step that rejects files missing required headers, or with manual edits (e.g., non-numeric OD). Unit tests / QA — scripted tests: known control wells → expected OD ranges; flag plates that fail. Logging & audit trail — write a processing log with file hash, timestamp, user, and processing outcome for Data Integrity. Version control & deployment — keep templates and code in git; deploy to the SAS server with scheduled jobs (not manual runs). Modular outputs — generate both the raw-mapped dataset and a “report” dataset the biostatistician expects so one run serves both needs. ... View more

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