Stephan Lanes, PhD, MPH

Principal Scientist

About Stephan

Dr. Stephan Lanes provides scientific oversight and strategic input for a variety of research projects as well as support for business development. He also serves as Principal Investigator for industry-sponsored and government-sponsored research projects. Dr. Lanes has consulting and industry experience, including 10 years of experience at Boehringer Ingelheim Pharmaceuticals, where he most recently held the position of Distinguished Clinical Scientist.

He is a longstanding member of the International Society for Pharmacoepidemiology (ISPE), where he has served on the Board of Directors and chaired the committee to update the Guidelines for Good Pharmacoepidemiology Practices (GPP). Dr. Lanes has published more than 100 papers on the design, conduct, and interpretation of epidemiologic studies in the peer-reviewed medical literature, and has presented his work at scientific meetings and to regulatory authorities worldwide.

Thought Leadership
Safety & Epidemiology
Real-World Evidence Complements Randomized Clinical Trials to Enhance Drug Safety and Effectiveness

Recently, researchers from the Safety and Epidemiology team at HealthCore conducted one of the first real-world studies investigating the safety of palbociclib, the most widely prescribed CDK4/6 inhibitor in the treatment of HR+/HER2- advanced stage breast cancer.

Health Economics & Outcomes Research
Predictive Modeling and Machine Learning: Sharpening the Focus of Real-World Evidence in the Age of Precision Medicine

Real-world evidence can usefully support precision medicine and faster regulatory approvals. The use of statistical methods to develop better case-identifying algorithms enables use of claims databases to study rare diseases and other targeted indications as well as more valid outcome ascertainment for safety and effectiveness research.

Safety & Epidemiology
The Power of Machine Learning

Machine learning combines advanced statistical methods with the results from validation studies to construct new case-identifying algorithms. We have found these methods to offer marked improvement in the accuracy with which we can identify patients of interest. These methods not only reduce bias from misclassification, which translates into more valid research, but we have also been able to identify patients with conditions for which there are no diagnosis codes in claims, such as cancer stage or biomarker status.