Patricia Stephenson, Sc.D.
Associate Director, Biostatistics
Dr. Patricia Stephenson is a Harvard graduate with over 10 years of experience working in oncology, including working with researchers at the Dana-Farber Cancer Institute. Dr. Stephenson has served as the lead statistician for multiple oncology studies, including a Phase 1 study pivotal for an NDA submission for accelerated approval. As a result, she was involved in several submission activities including supporting the statistical preparation and review for the Summary of Clinical Efficacy (SCE) and Summary of Clinical Safety (SCS) as well as preparation for a FDA Advisory Committee Meeting. Previously, Dr. Stephenson also served as the lead statistician for multiple Phase 1 and 2 studies in ovarian cancer, renal cell carcinoma, gastrointestinal stromal tumors, and non-small cell lung cancer.
Over the course of her career, she has led teams of data managers, statisticians, and programmers in providing biometrics support for many clinical studies and programs. Dr. Stephenson has also served as an FSP statistician for clients directly working with clinicians and researchers to develop manuscripts, abstracts and presentations and providing guidance on study design. Dr. Stephenson has experience with survival data techniques, PK analysis, and preparing reports for submission to the FDA.
Prior to joining Rho, Dr. Stephenson worked as a lead statistician at the Eastern Cooperative Oncology Group (ECOG) Coordinating Center at the Dana-Farber Cancer Institute. As a research fellow jointly at the Harvard School of Public Health and the Dana-Farber Cancer Institute, she collaborated on several ECOG lung and breast cancer studies (Phases 2 and 3) resulting in the development of over 11 manuscripts.
Why Oncology?
“Oncology is a rapidly growing area with new therapies that present more challenges and a greater need for innovation in statistical approaches.”
This is what drives Patricia:
“My work is centered around teamwork. I’m driven by the opportunity to collaborate with team members who bring different ideas and perspectives to the work that we do based on their role, experiences, and backgrounds.”
Content by Patricia Stephenson, Sc.D.
Blog
Maximizing the Use of Real-World Data (RWD): Beyond the Theory
Real-world data (RWD) has become a buzzword phrase in recent articles in clinical trial research. But there is often a difference between the idealistic and future use of RWD and how it can and is practically being used to advance drug development. What is RWD? How is RWD being applied now? What are the potential uses for it in the future?
Blog
Overcoming statistical challenges in rare disease drug development
Regulatory agencies like the FDA require substantial evidence of the drug’s effectiveness for its intended use and sufficient information to conclude that the drug is safe. However, flexibility is given in how the standard can be met given the challenges associated with the limited number of subjects available in rare disease.
Blog
Innovative Designs in Early-Stage Studies
Innovation in early-stage studies presents the best opportunity to streamline the drug development process. Such designs may not only reduce costs and accelerate timelines but give us better flexibility to address the questions of interest in an increasingly evolving clinical development landscape.
Blog
The Benefits and Risks of Subgroup Analysis for an Overall Survival Endpoint
Analyses conducted using the overall population can mask the extent of the benefit-risk in subgroups of patients. Thus, subgroup analyses are critical to fully understand the benefit-risk in pivotal oncology trials and guide regulatory decisions for approval and labelling. However, subgroup analyses also present several challenges especially for overall survival (OS) where larger sample sizes and longer follow-up are needed, and where early analyses of immature data may be conducted.
Blog
Statistical Design Considerations for Dose-Finding Oncology Trials
Since the introduction of the original 3+3 method, the available design options for dose escalation in early phase studies have expanded beyond traditional algorithm-based designs to include model-based designs.