The Longevity Data Crisis: Advanced SAS DATA Step, PROC SQL, and R Cleaning Workflows
Centuries of Life, Corrupted Records & Clinical Intelligence Engineering with SAS and R 1.Introduction In modern analytics ecosystems, data quality is not merely a technical concern it is a business survival requirement. Whether working in clinical trials, banking fraud detection, insurance adjudication, or retail intelligence systems, corrupted data can destroy analytical trust within minutes. Imagine a clinical trial investigating a life-extension therapy for elderly patients above 100 years old. A regulator discovers duplicate patient IDs, negative billing amounts, impossible ages like 250 years, inconsistent gender codes, malformed emails, and missing visit dates in the submission datasets. Suddenly: Statistical outputs become unreliable. SDTM validation rules fail. AI prediction models drift. Safety reports become misleading. Executive dashboards show incorrect survival rates. FDA reviewers question data traceability. This is exactly ...