Daniel C. Beachler, PhD, MHS

Director of Research, Safety & Epidemiology

About Daniel

Dr. Daniel C. Beachler leads pharmacoepidemiology research studies supporting medical product safety in both chronic and infectious disease areas. He has experience conducting validation studies and utilizing predictive modeling and machine learning methods in order to develop claims-based algorithms to improve cohort and outcome classification. He has also been involved with post-authorization safety studies (PASS), Risk Evaluation and Mitigation Strategy (REMS), and post-market requirement (PMR) studies.

Prior to joining HealthCore, Dr. Beachler was a Cancer Prevention Fellow at the National Cancer Institute, Division of Cancer Epidemiology and Genetics. Dr. Beachler has published dozens of manuscripts in the peer-reviewed medical literature. He has presented his work at scientific meetings and to regulatory authorities worldwide.

Thought Leadership
Safety & Epidemiology
HealthCore Impact Study: Using Background Incidence Rates to Inform Vaccine Safety

RCTs are comparative studies that use randomization and scientifically rigorous methods that are sufficient for approval-related decision making. However, these studies may not have sufficient sample sizes to detect rare adverse events or may exclude people with certain characteristics. Researchers then turn to real-world data (RWD) to gather more information on vaccines and monitor their safety and effectiveness in practice.

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.