When Brilliant Data Goes Bad: Mastering Transformation with SAS & R
Brilliant Minds, Broken Data: Transforming a “Top Scientists in the World” Dataset into Analytical Gold with SAS & R 1. Introduction Imagine you're working on a clinical trial dataset for a global oncology study. The stakes are high regulatory submission depends on your analysis. But when you open the dataset, chaos greets you: Patient ages include -5 and 250 Dates like 2023-02-30 Missing treatment groups labeled as "NULL", " " and NA Duplicate patient IDs Country names like "india", "INDIA", "Ind" This is not just messy it’s dangerous. Bad data doesn’t just slow you down; it destroys trust in analytics . In clinical trials, it can lead to incorrect efficacy conclusions , regulatory rejection, or even patient safety risks. This is where SAS and R shine. SAS dominates regulated environments (CDISC, SDTM, ADaM), while R provides flexibility and speed. Together, they form a powerful toolkit for data tran...