Real-World Evidence Practice (RWEP)
- Pharmaceutical, Biologic, & Medical Device Manufacturers
Project Summary
HPHCI’s Real-World Evidence Practice (RWEP) brings together expertise in epidemiology, data science, and large-scale research to design and deliver real-world evidence that informs regulatory, clinical, and policy decision-making.
Our work builds on decades of leadership in distributed data networks, methodological innovation, and collaborative research across healthcare systems.
Industry Expertise
Providing expertise in complex multi-site leadership and operations, the team implements methodologies informed by our decades of expertise in distributed research networks. We work with partners to develop and execute a range of studies to meet our sponsors’ business, regulatory, and scientific needs. These include:
- Multi-Site Methods and Coordination
- Validation Studies
- Pregnancy & Mother-Infant Linked Studies
- Real-World Evidence Research & Consulting including Postmarketing Requirement (PMR) & Postmarketing Commitment (PMC) Studies
- Comparative Effectiveness Studies
- Pragmatic Clinical Trials
- International Collaborations
Our Approach
Choosing the right approach requires a clear understanding of the question, the decision context, and the strengths and limitations of available data.
What Makes Our Methodology Unique
- Question-first design: we start with the scientific and decision-making question, then identify the most appropriate methods and data
- Experience across regulatory and applied settings: methods shaped by decades of work in real-world regulatory and clinical research
- Depth in distributed and multi-site research: designing methods that scale across large, complex data environments, including internationally
- Experience across therapeutic areas: allowing methods and insights to translate across diverse clinical contexts
- Focus on validity and interpretability: prioritizing approaches that produce credible, decision-relevant results
What We Do
We provide epidemiologic leadership and consulting to design studies that are scientifically robust, feasible, and aligned with regulatory, clinical, and policy needs:
- Study design and protocol development: selecting appropriate designs for specific research questions
- Causal inference and confounding control: including target-trial emulation, propensity score methods, instrumental variable approaches, and self-controlled designs
- Use of real-world data sources: guidance on claims, EHR, registry, and linked data
- Multi-site and distributed study design: methods for scalable, coordinated analyses
- Sequential and rapid surveillance: supporting active surveillance and timely evidence generation
- Validation and outcome confirmation: designing approaches to ensure outcome accuracy
- Sensitivity and quantitative bias analyses: evaluating assumptions and strengthening inference