Statistical Programming | SAS, R, Python, Shiny, Quarto | Data Visualization & Automation Tool | Developer | Clinical Trial | CDISC

Winkle Lu

LinkedIn Github winklelu1226@gmail.com

With over a decade of experience in clinical trial programming, I specialize in CDISC standards and regulatory deliverable — but I’m not standing still. I’ve embraced open-source tools like R, Shiny, and Python to drive automation and improve data visualization.

📘 Blog/Sharing | 👉 Presentations

Experience

Clinical Research Organization | Pharmaceutical Company | Statistical Programming

Education

Master of Public Health | Tzu Chi University

Performance Summary

  • 11+ years of clinical trial programming experience with deep expertise in SDTM, ADaM, and regulatory submission.

  • Led an 18-member team to complete COVID-19 Phase III programming within 3 months; results published in NEJM.

  • Skilled at cross-functional collaboration, providing medical-monitor support and creating publication-ready output.

  • Developed automation tools including an aCRF mapping system and review validation tools, boosting efficiency by 70%.

  • Presented at R/Pharma 2024 and ShinyConf 2025, demonstrating advanced R Shiny proficiency.

  • Recognized for consistently delivering high-quality work with exceptional efficiency, I have earned promotions at nearly every company I have worked for.

Therapeutic Area

  • Allergy / Immunology: Allergic Rhinitis

  • Cardiovascular: Cardiovascular Disease

  • COVID-19

  • Dermatology: Actinic keratosis, Angiosarcoma of Skin (disorder), Preventing Hypertrophic Scar, Psoriasis

  • Endocrinology: Diabetes Mellitus Type 2

  • Nephrology: Renal Impairment

  • Oncology: Multiple myeloma, Non-Small Cell Lung Cancer, Small Cell Carcinoma of Lung, Hepatocellular Carcinoma, Gastrointestinal Cancer, Malignant Melanoma, Malignant Neoplastic Disease

  • Transplantation: Rheumatoid Arthritis

Publications

  • Winkle Lu, Reviewing Clinical Data Efficiently with Shiny, ShinyConf 2025.

  • Winkle Lu, Presenting Clinical Results via CDISC-Compliant Shiny Apps, R/Pharma 2024.

  • Zhi-Sheng Lu, 2018, Combining quality of productivity and efficiency under highly pressure of lacking time – discussion by view of first-time quality, PharmaSUG – Beijing.

  • Shu-Hui Wen, Zhi-Sheng Lu, 2011, Factors affecting the effective number of tests in genetic association studies: A comparative study of three PCA-based methods, Journal of Human Genetics, 56, 428–435 [SCI].